Veterinary Thermographic Image Analysissumbaug/Veterinary Thermographic Im… · Web viewVeterinary Thermographic Image Analysis Project Number 7-64878 Report Number 4878-6 Scott

Veterinary Thermographic Image AnalysisProject Number 7- 64878Report Number 4878-6

Scott E Umbaugh, BSE, MSEE, PhDPatrick Solt, BSEE, MSCS, PhD Candidate

Computer Vision and Image Processing LaboratorySouthern Illinois University Edwardsville

January 21, 2009Fall 2008 Report

Submitted to:Dr. Catherine Loughin

Dr. Dominic Marino, Chief of StaffLong Island Veterinary Specialists

TABLE OF CONTENTS

Executive Summary……………………………………………………………………………...3Introduction…………………………………………………………………………………..…..4Software Development………………………………………………………………………...…4

Mask Creation Software…................................................................................................. 4Pattern Classification Software………………………………………………………..…4Texture Features………………………….……………………………………..……...…4Automatic Algorithm Development Tool……………….…………………………………6

Long Island Veterinary Specialists Trip ………………………………………………………6Overview………………………………………………………………………………….6Future Projects…………………...…………………………………………………….…7

Future Work ….…………………………………………………………………………………8References………………………………………………………………………………………..9Appendix A: Automatic Algorithm Development Tool User’s Manual ........................……10Appendix B: Future Projects ………………………………………………………………….26

Southern Illinois University EdwardsvilleDepartment of Electrical and Computer Engineering

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Executive Summary

Overview. Research and development for algorithms, software and experimental design continued. A trip to the Long Island Veterinary Specialists Clinic in New York was very enlightening and resulted in our better understanding of processes and procedures, as well as being introduced to future projects. Many more images of canines of the breed Cavalier King Charles Spaniel for the Chiari malformation (COMS) project were acquired and will enable us to develop more robust algorithms and to better test existing methods. This includes feature selection and color normalization. Images of normal and abnormal elbows were obtained and organized for future experiments with the Elbow Dysplasia project.

Software Development. Work was done for the following: 1) Enhancement of mask creation software, 2) Modification of pattern classification software for new experiments, 3) Research for new texture feature algorithms, 4) Creation of new texture feature extraction software and experimental design for comparison with previous texture features, 5) Initial implementation of an automatic algorithm development tool.

New York Trip. During this trip we toured the Long Island Veterinary Specialists facilities. This was important for us to observe firsthand the image acquisition process and to explore standardization of this process and observation of the methods. We also consulted the veterinarians to learn more regarding analysis and diagnostic uses of the thermographic images. Additionally, we conferred with the veterinarians and/or technicians in the development and use of the application-specific software. We were also able to discuss future projects with the veterinarians. These projects are as follows: 1) Elbow Dysplasia, 2) Feline Hyperthyroidism, 3) Type I Intervertebral Disc Disease, 4) Cranial Cruciate Ligament Tears of Stifles in Labrador retrievers, and 5) Three Post-operative Rehabilitation Methods for Cranial Cruciate Ligament Tears

Elbows Dysplasia Project. We were able to accomplish the following: 1) Organization of image files by image type, view, body part. 2) Creation of the first half of the mask images. 3) Design of experiments. 4) Design of analysis methods.


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Introduction

Research and development for algorithms, software and experimental design continued. A trip to the Long Island Veterinary Specialists Clinic in New York was very enlightening and resulted in our better understanding of processes and procedures, as well as being introduced to future projects. These projects are as follows: 1) Elbow Dysplasia, 2) Feline Hyperthyroidism, 3) Type I Intervertebral Disc Disease, 4) Cranial Cruciate Ligament Tears of Stifles in Labrador retrievers, and 5) Three Post-operative Rehabilitation Methods for Cranial Cruciate Ligament Tears

The acquisition of many more images of canines of the breed Cavalier King Charles Spaniel for the Chiari malformation (COMS) project will enable us to develop more robust algorithms and to better test existing methods. This includes feature selection and color normalization. Images of normal and abnormal elbows were obtained and organized for future experiments with the Elbow Dysplasia project.

Software Development

Mask Creation Software

We performed work for the enhancement of the mask creation software. This was necessary to facilitate the creation of a much larger number of masks due to the acquisition of many more images for current and future projects. The refinements to the methodology will allow for the masks to be created in a more streamlined fashion, and with better accuracy.

Pattern Classification Software

The pattern classification was modified for the new Elbow Dysplasia project. Here, we believe that features different than with the Chiari malformation (COMS) project will be appropriate. Specifically, new texture features and spectral features will be explored. Additionally, the leave-one-out testing technique will be enhanced by its integration into CVIPtools. Preliminary work has been done to allow for its completion during the Spring semester.

Texture FeaturesResearch has been undertaken for new texture features and the software is almost complete. It will be applied to the new Elbow Dysplasia project, and to the approximately 160 new COMS images. The new texture feature extraction software includes the following:

We use the second-order histogram of the gray levels based on a joint probability distribution model. The second-order histogram provides statistics based on pairs of pixels and their corresponding gray levels. The second-order histogram methods are also referred to as gray level co-occurrence matrix or gray level dependency matrix methods. These features are based on two


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parameters: distance and angle. The distance is the pixel distance between the pairs of pixels that are used for the second order statistics, and the angle refers to the angle between the pixel pairs. Typically, four angles are used corresponding to vertical, horizontal, and two diagonal directions. The pixel distance chosen depends on the resolution of the image and the coarseness of the texture of interest, although it is typical to use 1 or 2. To make the features rotationally invariant they can be calculated for all angles and then averaged (in CVIPtools the average and the range of these features are returned for the four angles).

Numerous features have been derived via these methods, but these five have been found to be the most useful: energy (homogeneity), inertia (contrast), correlation (linearity), inverse difference (local homogeneity) and entropy. If we let cij be the elements in the co-occurrence matrix normalized by dividing by the number of pixel pairs in the matrix, and assume a given distance and angle (direction), the equations are as follows:

Energy=∑i∑

jc

ij2

Inertia=∑i∑

j( i− j )2c ij

Correlation=1σ x σ y

∑i∑

j( i−μx )( j−μ y )c ij

where : μx=∑i

i∑j

c ij

and : μy=∑j

j∑i

cij

and :σ x2=∑

i( i−μx )

2∑j

c ij

and :σ y2=∑

j( j−μ y )

2∑i

c ij ;

InverseDifference=∑i∑

j

c ij

|i− j|; for : i≠ j

Entropy=−∑i∑

jcij log2 cij


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Automatic Algorithm Development Tool

The development of the Automatic Algorithm Development Tool (AADT) should prove very useful as the number of projects expands, and the nature of the projects varies. Similar to what was done with the previous software development where we automated the experimental process so that we could run millions of experiments instead of hundreds, the AADT software will allow for the investigation of many more permutations of image processing and feature extraction algorithms than was previously possible.

The AADT software will allow the user to select from a set of image processing filters, segmentation methods, morphological filters, and various postprocessing imaging functions. The user will be able to specify the range and the increment to be used with each of the imaging functions’ parameters. The software will then automatically run all the possible algorithmic permutations. The success measure will be tailored to the specific application, and we are currently investigating the useful success metrics for the veterinary projects. An application manual for the preliminary version of the software, CVIP-ATAT, is included in Appendix A.

Long Island Veterinary Specialists Trip

OverviewScott Umbaugh, principal investigator for the veterinary thermographic projects at SIUE, and Patrick Solt, PhD candidate at SIUE undertook an exploratory mission to Long Island New York to see the Long Island Veterinary Specialists facilities. Here we met with the principal investigators, Drs. Dominic Marino and Catherine Loughin and toured the facilities. We also met and discussed future projects with Drs. Brian Grossbard, Terry Arteaga, Tomas Infernuso, Dani Abrahams, Vivian Lau, and Micha Simons. We also met support staff member Maggie Byrnes and made arrangements for acquisition of images for the projects. We also had a phone conference with the company spokesman for the thermographic system and software. Here, we made arrangements to access the complete veterinary thermographic image database via the internet; but, after the fact, this was deemed to be impractical at this time.

The following tasks were accomplished: Tour of the Long Island Veterinary facilities. This was informative, enlighting and useful. Observed firsthand the image acquisition process to explore standardization of this

process and observation of the methods Consulted the veterinarians to learn more regarding analysis and diagnostic uses of the

thermographic images Conferred with the veterinarians and/or technicians in the development and use of the

application-specific software Discussion of future projects


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Future Projects

1. Thermographic Evaluation of Elbow Dysplasia. Comparison of normal and abnormal thermographic images of the elbows. Determine the optimal algorithm and imaging features for the classification.

2. Thermographic Evaluation of Feline Hyperthyroidism. Evaluation of normal feline thyroid and hyperthyroid glands thermographically. Assess the thermographic parameters of hyperthyroid cats thyroid glands pre and post radio-iodine treatment. Assess hyperthyroid cats thyroid glands pre and post clipping. Determine the optimal algorithm and imaging features for the classification.

3. Thermographic Imaging Characteristics of Type I Intervertebral Disc Disease in Dogs. To explore methods using infrared thermographic images and verify that they will correlate with findings of neurologic examinations, MRI and surgical findings in dogs with Type I intervertebral disc disease.

4. Thermographic imaging of cranial cruciate ligament tears of stifles in Labrador retrievers. To determine the accuracy of infrared thermographic imaging patterns in tears of cranial cruciate ligaments in Labrador retrievers. Infrared thermographic imaging findings will correlate with findings of pre-operative physical examination, and intra-operative findings in dogs with cranial cruciate ligament failure.

5. Thermographic evaluation of the canine stifle after cranial cruciate ligament repair and post-operative rehabilitation. Here we will compare the efficacy of three different post-operative rehabilitation methods: 1) electrical stimulation, 2) acupuncture, 3) cryotherapy. Thermographic images will be compared for the three different treatments by evaluation of the thermographs at various times and days post-op.


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Future Work

Complete the creation of the AADT software Run experiments for the Elbow Dysplasia project Analyze results from the Elbow Dysplasia project Run the previous experiments for the COMS project with the new image set Acquire images for the Cruciate Tears project, specifically the control, electro-

stimulation, acupuncture and cryotherapy groups. Acquire images for the Disc Disease/Chronic Pain (Grossbard) project Design, run and analyze experiments for the Cruciate Tears projects Design, run and analyze experiments for the Disc Disease/Chronic Pain (Grossbard)

project Acquire images for the Feline Hyperthyroidism project Design, run and analyze experiments for the Feline Hyperthyroidism project Research the use of neural networks for the analysis of veterinary thermographic images Investigate use of genetic algorithms for the analysis of veterinary thermographic images Research methods to determine the “best” feature set(s) Enhance the graphical user interface (GUI) for the automatic pattern classification and

application-specific software Explore finding of feature vector “norms” for various pathologies/abnormalities Explore multimodal image analysis Investigate use of support vector machines for analysis of veterinary thermographic

images


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References

1. Veterinary Thermographic Image Analysis, Project Number 7-64878, Report Number 4878-5, SIUE, August 20, 2008.

2. Computer Imaging: Digital Image Analysis and Processing , Scott E Umbaugh, The CRC Press, Boca Raton, FL, January 2005

3. Pattern Recognition Engineering, Morton Adler and Eric Smith, John-Wiley & Sons, NY, 1993

4. Pattern Classification, 2nd Edition, Richard Duda, Peter Hart, David Stork, John-Wiley & Sons, NY, 2001

5. Pattern Recognition, Sergios Theodoris and Konstantinos Koutroumbas, Academic Press, NY, 2006


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Appendix A: Automatic Algorithm Development Tool User’s Manual

Automatic CVIP Algorithm Test and Analysis Tool

Manual

Dr. Scott E Umbaugh

ECE DepartmentSouthern Illinois University Edwardsville


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Top GUI Layer(Input, output and result display)

(Visual studio C# .NET 2005)

Middle COM Layer(Communication layer)

（ Visual studio C++.NET 2005 ）

Bottom C Functions Libraries Layer

(Image data processing)（ Visual studio C++.NET 2005 ）

InputOutput

1. Overview

Image analysis is an important component for both Computer Vision and Image Processing; however, testing and analyzing image is a time-consuming and energy-consuming work. We designed this tool, Automatic CVIP (Computer Vision and Image processing) Algorithm Test and Analysis Tool (CVIP-ATAT), to speed up the process of searching some specific and/or generic algorithms.

In Section 1.1, the infrastructure of the tool will be introduced followed by the description of the GUI in Section 1.2.

1.1 InfrastructureCVIP-ATAT is composed of three layers. The top layer is Graphical User Interface (GUI) which is developed using C# .NET 2005. The middle COM layer is a communication layer which is developed using C++ .NET 2005. The bottom layer is CVIPtools C Functions Libraries, which is developed using C++ .NET 2005（see Figure 1）. The GUI is in charge of user’s input, output, and display analysis result. The COM layer is the connection between the GUI and CVIPtools C Functions Libraries. The CVIPtools C Functions Libraries consist of all image and data processing procedures and functions.In this manual, how to automate the image testing and analysis via the GUI system will be introduced.

1.2 GUIThe GUI of CVIP-ATAT is composed of seven interfaces: main interface, image input interface, algorithm input interface, image test algorithm, image comparison interface, image feature extraction interface and pattern classification interface.

Figure 1 CVIP-ATAT infrastructure


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The main interface is used to control other interfaces.The image interface is for user to input image. The algorithm interface is for user to define the algorithms being tested. The test interface is for user to run the test and display the test result. The image comparison interface is for user to compare images.The feature extraction interface is for user to extract image features. The pattern classification interface is for user to classify pattern.

2. Run CVIP-ATATIn order to run CVIP-ATAT, two files are required: CVIP-ATAT.exe and CVIPtools.dll. The GUI is implemented in file CVIP-ATAT.exe. All C functions for image analysis and processing is implemented in file CVIPtools.dll, which is invoked by CVIP-ATAT.exe. These two files have to be in the same folder. In order to run the tool, you just need to simply double-click file CVIP-ATAT.exe.

3. Create a new projectA new project should be created to hold the images that will be tested and analyzed. Different projects can be created to hold different types of images; however, just one project can be opened at one time. In order to create a new project, the following scenario should be followed:1. Select File, New project from the main menu to open the New project dialog box. See Figure

2 and Figure 3. 2. In the New project dialog box, key in a directory for your new project or use Browse button

to select a directory in which you wish your project to be. 3. In the New project dialog box, provide a name for your new project. Note that the tool will

create a new folder whose name will be identical with your project name and which will hold all of the files related to your new project.

4. Press OK button in the New project dialog box to complete the creation of your new project. You still can cancel the creation simply by pressing Cancel button.

The interface for your new project will look like the Figure 4.


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Figure 2 Main interface

Figure 3 Dialog for creating a new project


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Figure 4 Image interface

4. Open a projectThe following steps should be followed in order to open an existing project:1. Select File, Open project from the main menu to open the Open a project dialog box. See

Figure 2 and Figure 5. Note that if a project has been opened, then it must be closed before opening another project.

2. Select the project you want to open. To open a project means opening its configuration file: CVIPconfg.cfg.

3. Press Open button in the Open a project dialog to open the project. Press Cancel button to cancel the open.

The interface for opening a project will look like the figure 5.


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Figure 5 Dialog for opening a project

5. Input imageAfter creating a new project, user can import images in image interface. After opening an existing project, the user can add more images or delete some images for retesting. In order to add or delete image, the user should follow the following scenario:1. Image input interface is one of the three tab pages shown in Figure 4. If the image interface is

not been selected, click the images tab in Figure 4 to open it using mouse left key. 2. Press Add image button to add original images. After pressing add image button, an add

image dialog will appear as shown in Figure 6. User can add a single original image or multiple original images at one time. If add multiple original images, user need hold the Ctrl key down, when using mouse to select several images.

3. Since ideal images should match the added original images, user cannot add ideal images if there is no original images exist. So in order to add ideal images. Firstly user should check the original images that need ideal images by using Ctrl key and mouse left key to choose them from the original image list table. Then press Add ideal button to open the dialog for adding ideal image. At last, choose the ideal image. An ideal image can be matched to several original images at one time.

4. User can delete all the original images (including ideal images) by using Delete all image button or delete part of the original images using Delete selected image button. If user wants to delete part of the original images, the original images that will be deleted should be selected first by using Ctrl key and mouse left key.

5. If user just wants to delete ideal images, Delete selected ideal and Delete all ideal image


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buttons can be used to do so. Notes:

The original images are used for testing and the result images (output images of original images) are compared with the ideal images if exist any; however, the ideal images must match the result images. It means that ideal images and their corresponding result images must have the same bands with regard to XOR-Aera, subtraction-Energy, SNR-Error and RMS-Error comparisons. Besides, the ideal images and the result images must have the same size in terms of SNR-Error and RMS-Error comparisons.

Figure 6 Dialog for adding images

6. Input algorithmUser can input algorithms which will be tested on the images using algorithm input interface, see Figure 7.In order to input algorithm, the user should follow the following steps:1. Algorithm input interface is one of the three tab pages shown in Figure 4. If the algorithm

input interface is not been selected, click the processes tab in Figure 4 to open it using mouse left key.

2. User can select an algorithm through the ComboBox named Select processes. After an algorithm is selected, user can adjust parameters for that algorithm. Figure 8 displays how to select an algorithm.

3. User has to specify a stage for every algorithm through the ComboBox named Select stage. Notice that the image testing process can be broken down to several stages and more than one algorithm can be applied for each stage.


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4. User may need to adjust the parameters’ values of every algorithm. If a parameter’s lower value is 1, increment value is 2 and upper value is 5, then the value for this parameter could be 1, 3 or 5. Because of this, there could be more than one option for each algorithm.

5. After selecting algorithm and its stage, user needs to press Add process button to add selected algorithm and its stage into algorithm list table.

6. User can repeat the step 3, step 4 and step 5 until all of the required algorithms have been selected.

7. User can delete one or multiple algorithms listed in the list table easily by checking them using mouse left key, then press Delete checked process button.

8. If user wants to delete all of the algorithms in the list table, just press the Delete all process button.

9. User can adjust the order of the algorithms in the list table by selecting one of the algorithms, then move it using Up button or Down button.

10. The amount of images on which the algorithm will be tested and the amount of options for one image are displayed at the bottom of the algorithm input interface.

Figure 7 Algorithm input interface


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Figure 8 Select an Algorithm from ComboBox

7. TestThe test interface (Figure 9) is used to test algorithms on images and display the test results. The following steps should be followed:1. Test interface is one of the three tab pages shown in Figure 4. If the test interface is not

selected, click the test tab in Figure 4 to open it using mouse left key. 2. User just needs to press down the Run button. Then all of the selected algorithms will be

tested on all of the selected images automatically.3. During the test, a progress bar is used to display the progress.4. Because of multi-thread technique, user can pause or stop the test process at any time.5. The test results are displayed in the result list table. 6. User can delete part of the test result by checking the items in the list table, then press Delete

checked result button.7. User can delete all of the test result by pressing Delete all result button.


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Figure 9 Test interface

8. ComparisonComparison interface is used to compare images with their corresponding ideal images. The scenario is given as follows:1. Select Comparison from main menu to open comparison interface, see Figure 10 and Figure

11.2. Select one method for comparison from RMS-Error, SNR-Error, Subtraction-Entropy and

XOR-Area methods. User can adjust the parameters of every comparison method easily if necessary.

3. If remapping image is necessary, then check the Remap image first box. 4. After finishing the step 2 and step 3, user just needs to press down Analyze button. Then the

comparison will run automatically. 5. Because of multi-thread technique, user can stop the analyzing process at any time. 6. The comparison results will be listed in the tab pages. Since there are four comparison

methods, there are four tab pages respectively.


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Figure 10 Comparison interface for RMS-Error

Figure 11 Comparison interface for XOR-Area


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9. Feature extractionFeature extraction interface is used to extract images’ features. In order to extract images’ features, the following steps should be followed:1. Select Feature from main menu to open feature extraction interface (see Figure 2).2. Select object. There are two methods for object: Middle of image and K largest objects. If

user chooses middle of image, it means the object that user is interested in is in the middle of the image. This is the easiest way for feature extraction. Currently, we use this way for image feature extraction. If user chooses K largest objects, it means user wants extract features for K largest objects in the images.

3. Extracted features could be for Training set or Testing set, user has to specify one set. This will benefit pattern classification.

4. User needs to specify the features that need to be extracted. In this software, there are five types of features: binary object features, RST-invariant moment based features, histogram features, texture features, and spectral features. For each type of features, there are several different sub-features (see Figure 12). User can select part of the sub-features by checking the boxes nearby the sub-features or user can select all of the sub-feature by pressing Select all button.

5. By following step 2, step 3 and step 4, user can press down the Analyze button to extract features automatically.

6. Because of multi-thread technique, user can stop the analyzing process at any time. 7. The feature extraction results will be displayed in the list table.

Figure 12 Feature extraction interface


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10. Pattern classificationPattern classification interface is used to classify pattern. In order to classify pattern the following steps should be followed:1. Select Pattern classification from main menu to open pattern classification interface (see

Figure 13).2. Select Data normalization method. There are six methods for data normalization: no

normalization, range-normalize, unit vector normalization, standard normal density normalization, min-max normalization, and softmax scaling.

3. Select distance and similarity measures method. There are four methods for distance measures: Euclidean distance, city block or absolute value metric, maximum value metric, and Minkowski distance. There are two methods for similarity measures: vector inner product and Tanimoto metric.

4. Select classification algorithms. There are three classification algorithms: nearest neighbor, K-nearest neighbor, and nearest centroid.

5. By following step 2, step 3 and step 4, user can press down the Analyze button to classify pattern automatically.

6. Because of multi-thread technique, user can stop the analyzing process at any time.

Figure 13 Pattern classification interface

11. Save projectThere are two ways to save project:Way 1: save project without closing project by selecting Save project from main menu to save project directly.


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Way 2: save project while closing project. The following steps should be followed: 1. When user closes project or exits software, the save project dialog will pop out(See

Figure 14).2. Press Yes button to save project, No button to exit software without save, or Cancel

button to give up save operation.

Figure 14 Dialog for saving project

12. Close projectThere are three ways to close project.Way 1: close project using menu by select Close project from main menu. Way 2: close project using main interface. In the main interface, click mouse left key on the close button at the top-right of the interface. Actually this method will exit software. Way 3: close project using test interface. In the test interface, click mouse left key on the close button at the top-right of the interface.

13. Exit softwareThere are two ways to exit software.Way 1: exit software through menu: select Exit project from main menu to exit software. Way 2: exit software through main interface. In the main interface, click mouse left key on the close button at the top-right of the interface.


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14. Database Database is used to store input data, output data, and analysis result displayed on the GUI. This software has four databases: CVIPATAT_database.mdb, feature_database.mdb, compare_database.mdb, and pattern_database.mdb. Figure 15 is an example for compare_database.mdb. The CVIPATAT_database.mdb is composed of 3 tables: Images table, Processes table, and TestResult table. The compare_database.mdb is composed 4 tables: CompareRMS table, CompareSNR table, CompareSub table, and CompareXOR table. The feature_database.mdb just has one feature table. The pattern_database.mdb just has one pattern table. Images table is used to store all input images’ names, paths, their ideal images’ names and paths if ideal images exist. Processes table is used to store the algorithms and their corresponding parameters. TestResult table is used to store the output images’ names, paths, and their corresponding algorithms and parameters. CompareRMS table is used to store the results of RMS-Error comparison. CompareSNR table is used to store the results of SNR-Error comparison. CompareSub table is used to store the results of Subtraction-Enerpy comparison. CompareXOR table is used to store the results of XOR-Area comparison. Features table is used to store features. Pattern table is used to store the result of pattern classification. Figure 16 gives an example of CompareRMS table.In order to open these databases, we need use Microsoft Office Access 2003. After opening these databases, we can explore, search and modify data in the databases easily.


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Figure 15 Tables for Compare Database

Figure 16 RMS-Error Comparison Result Table


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Appendix B: Future Projects

Thermographic Evaluation of Feline Hyperthyroidism

Investigators: Theresa Arteaga, Catherine Loughin, Dominic Marino

Research Problem: Because of fluctuations in daily basal free thyroid levels in cats, 2-13% of hyperthyroid cats will have normal T4’s. Also, if a cat has a concomitant disease process, often hyperthyroid cats will have sick euthyroid syndrome and have normal T4’s. Nuclear scintigraphy and repeated blood tests are performed, which is expensive and in the case of nuclear scintigraphy only performed at select referral hospitals.

Objective: The purpose of this study is to evaluate normal feline thyroid and hyperthyroid glands thermographically. Secondly, to assess the thermographic parameters of hyperthyroid cats thyroid glands pre and post radio-iodine treatment. Third to assess hyperthyroid cats thyroid glands pre and post clipping.

Background: Hyperthyroidism is the most common feline endocrine disease. A cat with clinical signs of hyperthyroidism (polyphagia,ventro-cervical mass, pu/pd, fractious..) and a high free T4 is considered hyperthyroid. However because of daily fluctuations in basal free T4 levels, 2-13% of hyperthyroid cats will have a normal T4. This requires repeat testing, which is expensive and time consuming. Also if a cat has a concomitant disease often they will display sick euthyroid syndrome, in which a hyperthyroid cat will have normal T4 levels. In this case, a T3 suppression test is performed or nuclear scintigraphy to arrive at a definitive diagnosis. This is difficult as the T3 suppression test is another blood test in what is often a fractious cat and nuclear scintigraphy is both expensive and not readily available except at select referral hospitals. Thermal imaging has been applied in many fields in human medicine, including sports medicine, cancer diagnosis and breast diseases. In veterinary medicine it is used for orthopedic disease in horses and small animals. The thyroid gland because of its rich vascularity and superficial anatomic position may be an ideal organ for thermographic evaluation in animals. More importantly if a hyperactive nodule is a center of increased blood flow and chemical activity, it could also be a center of heat production that is detectable by thermal sensing. Hypothesis: Euthyroid feline thyroid glands will have decreased thermographic values compared to hyperthyroid feline glands. Hyperthyroid cats treated with radio-iodine will have decreased thermographic values post treatment versus pre-treatment. Cats that are clipped will have increased thermographic parameters versus unclipped cats.

Materials and Methods:Patient Parameters: Age, sex, neutered, weight of statistically significant numbers of client owned control cats and hyperthyroid cats. All cbc/chemistry/ua/T4 values or concurrent disease will be accounted for.

Experimental Design: A prospective study comparing ten “normal” control cats versus hyperthyroid cats both before and after therapy. The variables compared will be normal


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thermographic values of feline thyroid glands versus hyperthyroid cats. The values of cats thermographically pre and post radio-iodine treatment. The thermographic values of cats pre and post clipping. The thermographic values as they compare to T4 levels. Statistical Analysis: Significance: The purpose of this study:

1. Establish normal feline thyroid gland thermographic parameters (weight, sex, neuter status, breed)

2. To see if hyperthyroid cats differ significantly from euthyroid cats thermographically

3. To see if hyperthyroid cats T4 levels correlate with their thyroid gland thermographic values

4. To see if there are thermographic changes in pre and post radio-iodine treated thyroid glands in hyperthyroid cats

5. To see if clipping has significance in thermographic evaluation of hyperthyroid cat thyroid glands

If there is a statistically significant correlation in any of these variables then thermography can be of value as a diagnostic tool for detection, treatment assessment and recurrence of hyperthyroid cats.

ReferencesSamuels, Barry: Thermography: A Valuable Tool in the Detection of Thyroid Disease, Diagnostic Radiology, 102:53-62, Jan 1972Clark, Orlo, greenspan, Coggs, Goldman: Evaluation of Solitary Cold Thyroid Nodules by Echography and Thermography, The American Journal of Surgery, 1975Helmy, Ahdy, Holdmann, Rizkalla: Application of Thermography for Non-Invasive Diagnosis of Thyroid Gland Disease, IEEE Transactions on Biomedical Engineering, Vol 55, No. 3, 3/2008Feldman and Nelson: Veterinary Endocrinology, Hyperthyroidism in Cats, 2001


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Thermographic Imaging Characteristics of Type I Intervertebral Disc Disease in Dogs.

Investigoator: Brian Grossbard

Research Purpose: To assess the accuracy of infrared thermographic imaging in predicting the location of intervertebral disc herniation with type I IVDD in small breed dogs.

Background: Intervertebral disc herniation is a common cause of pain and myelopathy in canine patients. Alterations in perfusion resulting in clinically detectable changes on infrared thermography have been documented in human patients suffering from intervertebral disc herniation. The reported sensitivity and specificity of thermographic imaging in determining the precise location and etiology pain has been mixed and previous studies have been limited by operator dependence and blindedness. In equine studies, various imaging characteristics (Turner Vet Clin NA April 2001) have been shown to correlate with root signatures and other compressive spinal conditions. Magnetic resonance imaging (MRI) is currently considered the gold standard for the diagnosis of intervertebral disc disease in small animals. Limitations of MRI include the need for general anesthesia, availability of equipment and trained technical staff and financial cost. Additionally, it is not uncommon to have multiple sites of intervertebral disc herniation to varying degrees on MRI studies in breeds that are predisposed to the condition. Advantages of infrared thermographic imaging include its minimal invasiveness (no sedation or anesthesia is required), relatively low cost, speed of image acquisition, lack of radiation exposure and objectivity. Hypothesis: Infrared thermographic imaging findings will correlate with findings of neurologic examinations, MRI and surgical findings in dogs with Type I intervertebral disc disease. Materials and Methods:

Patient selection:Clinical patients: Small breed dog (<12 kg) (Dachshunds, Lhasa Aphsa, Beagle, Poodle) with an acute onset T3-L3 myelopathy and no history of external trauma. Control group: X dogs matched for breed, body weight and age without any history of neurologic impairment or spinal hyperpathia and currently free of any neurologic deficits.

Experimental Design: Patients presenting with a history of acute onset T3-L3 myelopathy will receive a full neurologic assessment upon presentation. Duration of clinical signs, neurologic status (scale), age, breed, sex, previous treatment (steroids, pain meds, methocarbamol), and additional medical conditions (diabetes, hypothyroidism, other) will be recorded for each patient. Following examination, the patients will sit in a hospital room for 30 minutes to allow acclimation with the environmental temperature (21 C). Metallic markers will be placed lateral to the vertebral column to mark vertebrae T1 and T13 and the L7-S1 intervertebral space. (confirmed later with radiographs ?)

Thermographic imaging:


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1. Thermographic imaging of the spinal column (dorsal and lateral) will be obtained after animals have not been handled for 30 minutes. Animals will be induced using ___(Standard induction protocol), with pretreatment with intravenous steroids. 2. Thermographic imaging will be repeated under general anesthesia. (dorsal and lateral)3. Ventrodorsal and lateral spinal radiographs will be obtained to rule out congenital vertebral abnormalities (hemivertebrae, block vertebrae) that may alter the thermographic image.4. MRI examination of the spinal column will be performed. Individual intervertebral disc herniations will be noted and given subjective scoring (mild, moderate, severe). In addition, measures measure of intervertebral disc hydration, degree of spinal cord compression/disc extrusion and location of disc extrusion (right, left, central) will also be recorded.5. Thermographic imaging will be repeated 60 minutes after shaving of the dorsal vertebral column. (Dorsal and lateral)6. Decompressive surgery to confirm IVDD in clinical animals.7. Follow up thermographic evaluations will be obtained 2 weeks and 2 months post-surgery.

Statistical Analysis: Results of diagnostic imaging (thermography and MRI) will be evaluated by ____ (radiologist, surgeon) and correlations between the two modalities, clinical findings and surgical findings will be determined.

Research Facilities: All examination, thermographic imaging, MRI examinations and surgical procedures will be performed at Long Island Veterinary Specialists.

Acknowledgements: Influence of steroids, PEG or analgesic medications, (presurgical treatment or prior to presentation) may be unknown, outside the scope of the paper.

References: -Zhang HY, Kim YS, Cho YE. Thermatomal changes in cervical disc herniations. Yonsei Med J. 1999 Oct;40(5):401-12.-Maigne JY, Treuil C, Chatellier G. Altered Lower Limb Vascular Perfusion in Patients With Sciatica Secondary to Disc Herniation. Spine, July 15, 1996, 21:14.-So YT, Aminoff MJ, Olney KO. The role of thermography in the evaluation of lumbosacral radiculopathy. Neurology, 1989’39:1154-1158. -Turner TA. Diagnostic Thermography. Vet Clin North Amer Equine Practice: Vol 17; 1 April 2001:95-113. -Tunley BV, Henson FMD. Reliability and repeatability of thermographic examination and the normal thermographic image of the thoracolumbar region in the horse. Equine Vet J. 2004; 36 (4) 306-312.

-Zhang HY, Kim YS, Cho YE. Thermatomal changes in cervical disc herniations. Yonsei Med J. 1999 Vol. 40; 5: 401-412.


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Thermographic imaging of cranial cruciate ligament tears of stifles in Labrador retrievers.

Investigator: Tomas Infernuso

Research Purpose: To determine the accuracy of infrared thermographic imaging patterns in tears of cranial cruciate ligaments in Labrador retrievers.

Background:Stifle instability secondary to a cranial cruciate ligament tear is a common cause of lameness in canine patients. The high incidence of cranial cruciate ligament failure suggests that there is an underlying cause of premature degeneration of the ligament, which could be associated with aging (especially large breed dogs), conformational abnormalities (straight rear limbs), and immune-mediated polyarthropathy.

Alterations in perfusion resulting in clinically detectable changes on infrared thermography have been documented in human patients suffering from chronic knee pain. Thermography has been shown to be useful in differentiating pain-free individuals from patients reporting knee pain. A good relationship was found between changes in pain intensity, and changes in symmetry of heat patterns for most patients with chronic knee pain.

Physical examination and diagnostic imaging such as radiographs, magnetic resonance imaging (MRI) and arthroscopy are currently considered to identify cranial cruciate deficient stifles, in small animals. Limitations of MRI and arthroscopy include the need for general anesthesia, availability of equipment and trained technical staff and financial cost. Radiographs are helpful in ruling out other causes of stifle joint lameness.

Advantages of digital infrared thermographic imaging include its minimal invasiveness (no sedation or anesthesia is required), relatively low cost, speed of image acquisition, lack of radiation exposure and objectivity.

Hypothesis: Infrared thermographic imaging findings will correlate with findings of pre-operative physical examination, and intra-operative findings in dogs with cranial cruciate ligament failure.

Materials and Methods:Patient selection:Clinical patients: 10 Labrador retrievers with acute or chronic onset of non weight-bearing or partial weight-bearing hind limb lameness.Control group: Dogs matched for breed, body weight and age without any history of lameness and currently free of any orthopedic and neurologic deficits.

Experimental Design: Patients presenting with a history of acute or chronic onset of hind-limb lameness will receive a full orthopedic and neurologic assessment upon presentation. Duration of clinical signs, orthopedic status, age, breed, sex, and previous treatment with nonsteroidal anti-inflammatory medications will be recorded for each patient. Following examination, the


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patients will sit in a hospital room for 30 minutes to allow acclimation with the environmental temperature (21 degree Celsius).

Thermographic imaging:

1. Thermographic imaging of stifle (cranial, lateral and medial) taken after animals not handled for 30 minutes.

At this point, animals were induced using standard induction protocol.2. Thermographic imaging repeated under general anesthesia, (cranial, lateral and medial).3. AP and lateral stifle radiographs to rule out abnormalities (infection, soft tissue neoplasia, and osteoarthritis) that may alter the thermographic image.4. 3rd thermographic imaging 60 minutes after shaving of the hind limb centered over the stifle joint: cranial, lateral and medial.5. Extracapsular lateral imbrication (ECLI) with intra-articular explore to confirm anterior cruciate tear in clinical animals.6. Follow up thermographic evaluations and gait analysis 1, 3, 5, 7 and 14 days after surgery.

Statistical Analysis: Results of diagnostic imaging (thermography) and pre-operative physical examination will be evaluated and correlations among thermographic, clinical and surgical findings will be determined.

Research Facilities:All examination, thermographic imaging, and surgical procedures will be performed at Long Island Veterinary Specialists.

References:

1.Sherman RA.et all Thermographic correlates of chronic pain: Analysis of 125 patient incorporating evaluations by a blind panel. Arch Phys Med Rehabil 1987 May; 68 (5 pt 1): 273-9

2. Um SW, et all. Thermographic evaluation for the efficacy of acupuncture on induced chronic arthritis in the dog. J. Vet Med Sci. 2005 Dec; 67 (12): 1283-4

3.Devereaux MD. et all. Thermographic diagnosis in athletes with patellofemoral arthralgiaJ. Bone Joint Surg Br. 1986 Jan; 68(1): 42-4

4.Siegel MG The use of computerized thermography in the evaluation of non-traumatic anterior knee painOrthopedics 1987 May; 10(5): 825-30

5.Stein LE et all A comparison of steady state and transient thermography techniques using a healing tendon model Vet Surg 1988 March-April; 17 (2):90-6

6.Peterson J. Assessment of rheumatoid inflammation in the knee joint. A reappraisal. Ann Rheum Dis 1978 feb; 37 91) 48-52

7.Ben DJ Infrared Thermographic imaging in the detection of sympathetic dysfunction in patient with patellofemoral syndrome J Manipulative Physiol Ther 1992 Mar-April; 15(3): 164- 70

8.Rajapakse C. Thermography in the assessment of peripheral joint inflammation ..a re-evaluation Rheumatol Rehabil 1981 May; 20(2): 81-87


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Thermographic evaluation of the canine stifle after cranial cruciate ligament repair and post-operative rehabilitation with electrical stimulation.

Investigator: Dani Abrahams

Objective:The purpose of our study is to use thermography to assess the change in the post-

operative cranial cruciate repaired stifle blood flow and autonomic funtion, to assess the change in the thermographic pattern and to objectively assess rehabilitation via electrical stimulation effects on the thermographic pattern.

Background:Cranial cruciate ligament rupture is a common orthopedic problem in canines. Surgical

stabilization is the treatment of choice. Intensive post-operative rehabilitation has been shown to impact surgical outcome in human patients; for example, electrical muscle stimulation has been utilized to increase quadriceps strength in atrophied limbs.2-7 In dogs, electrical stimulation has been subjectively noted to improve limb function over cage rest with gradual return to activity alone; however, this subjective assessment of clinical improvement could not be corroborated by force-plate analysis.1

Thermography is an objective, non-invasive imaging technique that has been utilized to diagnose, monitor, and direct therapy in various inflammatory conditions in horses and humans for several decades.10-12 Thermography can detect subclinical disease that cannot be identified radiographically, 10-11 does not require that animals undergo general anesthesia, as with MRI and CT, and may be more reliable than force-plate analysis. Normal thermographic patterns have recently been identified in the limbs of healthy dogs to which diseased or injured states can be compared.8 We have yet to apply this information to monitoring of our post-operative cranial cruciate deficient stifle patients and evaluation of current therapeutic modalities.

Hypothesis:Thermographic patterns of the post-operative stifle will change with rehabilitation via

electrical stimulation.

Material and Methods:Patient Parameters:20 dogs with documented cranial cruciate ligament tears repaired via extracaplsular

lateral imbrication (ECLI). Ten dogs will serve as controls and 10 dogs will be rehabilitated via electrical stimulation.

Experimental Design:10 dogs that have been evaluated and determined to have a cranial cruciate ligament tear

will undergo repair via ECLI and rehabilitation via electrical stimulation. A TENS unit will be utilized for 20 minutes once daily on days 2, 3, and 4 post-operatively. The TENS unit will be set at 250 microseconds with a frequency of 9 Hz with variable intensity according to patient comfort level. Each dog will be evaluated by thermography and gait analysis pre-operatively


32

(full haircoat and clipped) and days 3, 5, 7, and 14 post-operatively. Comparison between results of a gait analysis and thermographic findings will be performed at each assessment.

Statistical Analysis:Statistically significant data correlations will be identified.

Research Facilities:Portable infrared thermograph located at the Long Island Veterinary Specialists facility,

and TENS unit located at the Pet Wellness Center in Plainview, NY.

Rationale:The rationale of this study is to identify if thermography can be utilized to adequately

monitor recovery from cranial cruciate ligament repair, as well as objectively assess the effectiveness of rehabilitation via electrical stimulation.

Significance:The significance of this study is that if thermography can objectively demonstrate that

electrical stimulation is an effective form of therapy in post-operative canine stifles, it can also be utilized to guide treatment and determine the optimal therapeutic regimen. Alternatively, it may indicate that electrical stimulation is not an effective therapeutic modality in cranial cruciate deficient canine stifles or that thermography is not a viable option for objective evaluation of post-operative progress.

References1. Johnson JM, Johnson AL, Pijanowski GJ, Kneller SK, Schaeffer DJ, Eurell JA, Smith

CW, Swan KS. Rehabilitation of dogs with surgically treated cranial cruciate ligament-deficient stifles by use of electrical stimulation of muscles. Am J Vet Res 58(12):1473-8, Dec 1997.

2. Wright RW, Preston E, Fleming BC, Amendola A, Andrish JT, Bergfeld JA, Dunn WR, Kaeding C, Kuhn JE, Marx RG, McCarty EC, Parker RC, Spindler KP, Wolcott M, Wolf BR, Williams GN. A systematic review of anterior cruciate ligament reconstruction rehabilitation: part II: open versus closed kinetic chain exercises, neuromuscular electrical stimulation, accelerated rehabilitation, and miscellaneous topics. J Knee Surg 21(3):225-34, Jul 2008

3. Snyder-Mackler L, Ladin Z, Schepsis AA, Young JC. Electrical stimulation of the thigh muscles after reconstruction of the anterior cruciate ligament. Effects of electrically elicited contraction of the quadriceps femoris and hamstring muscles on gait and on strength of the thigh muscles. J Bone Joint Surg Am 73(7):1025-36, Aug 1991

4. Snyder-Mackler L, Delitto A, Stralka SW, Bailey SL. Use of electrical stimulation to enhance recovery of quadriceps femoris muscle force production in patients following anterior cruciate ligament reconstruction. Phys Ther 74(10):901-7, Oct 1994

5. Snyder-Mackler L, Delitto A, Bailey SL, Stralka SW. Strength of the quadriceps femoris muscle and functional recovery after reconstruction of the anterior cruciate ligament. A prospective, randomized clinical trial of electrical stimulation. J Bone Joint Surg Am 77(8):1166-73, Aug 1995


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6. Fitzgerald GK, Piva SR, Irrgang JJ. A modified neuromuscular electrical stimulation protocol for quadriceps strength training following anterior cruciate ligament reconstruction. J Orthop Sports Phys Ther 33(9):492-501, Sep 2003

7. Delitto A, Rose SJ, McKowen JM, Lehman RC, Thomas JA, Shively RA. Electrical stimulation versus voluntary exercise in strengthening thigh musculature after anterior cruciate ligament surgery Phys Ther 68(5):660-3, May 1988

8. Loughin CA, Marino DJ. Evaluation of thermographic imaging of the limbs of healthy dogs. Am J Vet Res 68(10):1064-9, Oct 2007

9. Um SW, Kim MS, Lim JH, Kim SY, Seo KM, Nam TC. Thermographic evaluation for the efficacy of acupuncture on induced chronic arthritis in the dog. J Vet Med Sci 67(12):1283-4, Dec 2005

10. Purohit RC, McCoy MD. Thermography in the diagnosis of inflammatory processes in the horse. Am J Vet Res 41(8):1167-74, Aug 1980

11. Vaden MF, Purohit RC, McCoy MD, Vaughan JT. Thermography: a technique for subclinical diagnosis of osteoarthritis. Am J Vet Res 41(8):1175-9, Aug 1980

12. Gratt BM, Sickles EA, Wexler CE. Thermographic characterization of osteoarthrosis of the temporomandibular joint. J Orofac Pain 7(4):345-53, Fall 1993


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Thermographic evaluation of the stifle after cruciate ligament repair and post-operative acupuncture

Investigator: Vivian Lau Research Problem: Does acupuncture affect the post-operative stifle thermographic patterns of dogs undergoing extracapsular lateral imbrication (ECLI) surgery for cranial cruciate ligament (CCL) tears?

Objective: The purpose of our study is to determine the thermographic effects of acupuncture as a post-operative rehabilitation modality for dogs undergoing ECLI surgery for the repair of torn cranial cruciate ligaments.

Background: Cranial cruciate ligament ruptures are one of the most common orthopedic injuries of canine veterinary patients. Several surgical treatments are available but the extracapsular lateral imbrication technique remains one of the most popular methods of repair. Recovery in the immediate post-operative period may be aided by various rehabilitation modalities which include ice, electrical stimulation, and alternative therapies like acupuncture. Objective evaluation and comparison of the efficacy of these modalities is difficult to achieve. Thermography provides a non-invasive means of obtaining reliable and consistent evaluation of canine limbs and osteoarthritis (1,2).

As alternative therapies like acupuncture continue to gain popularity in veterinary medicine, the objective evaluation of such therapies and their value relative to more traditional treatments becomes increasingly important. Acupuncture has previously been used with positive effects in the management of arthritis and orthopedic/neurologic disease in canine and human patients (3,4). The efficacy and value of acupuncture as a rehabilitation modality for CCL injuries in canine patients, however, is unknown.

Hypothesis: Acupuncture does have an affect on the post-operative stifle thermographic pattern of dogs undergoing ECLI surgery for CCL tears. Acupuncture is a viable post-operative rehab modality and may compare favorably to other rehab modalities like ice or electrical stimulation.

Material and Methods: Patient Parameters: Age, sex, and weight of 20 dogs with CCL tears undergoing ECLI surgery will be collected for the purpose of this study.

Experimental Design: Thermographic patterns (full hair coat and clipped) of 10 dogs will be collected pre-operatively. Post-operative acupuncture will be performed twice each week for four weeks. Thermographic imaging will be done before and after each acupuncture treatment followed by gait analysis. The thermographic patterns of these dogs will be compared to those of the 10 dogs not receiving post-operative acupuncture. Thermographic data will be collected and analized.

Statistical Analysis: Statistically significant data correlations will be identified.


35

Research Facilities: Stand-mounted infrared camera with focal plane array amorphous silicone microbolometer and standard laptop computer located at the Long Island Veterinary Specialists facility. Images will be analyzed with an image-processing software program custom-designed for thermography.

Rationale: The rationale of this study is to identify if acupuncture, an increasingly popular rehab modality, has any effects on the post-operative thermographic pattern, a viable means of evaluating inflammation, in canine patients undergoing CCL injury repair.

Significance: The significance of this study is that if acupuncture is shown to have a beneficial effect on post-ECLI stifle thermographic patterns, it can be recommended as a viable rehab modality to pet owners seeking to aid their dog in the post-operative recovery period.

References

1. Loughin, CA and D, Marino. Evaluation of thermographic imaging of the limbs of healthy dogs. AJVR 68:1064-1069, 2007

2. Thermography: a technique for subclinical diagnosis of osteoarthritis. Am J Vet Res. 1980 Aug;41(8):1175-9.

3. Um SW, Kim MS, Lim JH, Kim SY, Seo KM, Nam TC. Thermographic evaluation for the efficacy of acupuncture on induced chronic arthritis in the dog. J Vet Med Sci. 2005 Dec;67(12):1283-4Oh JH, Bai SJ, Cho ZH, Han HC, Min SS, Shim I, Lee HJ, Lee H, Lee B. Pain-relieving effects of acupuncture and electroacupuncture in an animal model of arthritic pain. Int J Neurosci. 2006 Oct;116(10):1139-56.


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http://www.ncbi.nlm.nih.gov/pubmed/16923683?ordinalpos=117&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum

The thermographic evaluation of the stifle after cranial cruciate ligament repair and post-operative cryotherapy.

Investigator: Micha C. Simons

Research Problem: Cryotherapy is a commonly used post-operative rehabilitation modality and there is little published data on its efficacy in canine patients.

Objective: The purpose of our study is to compare the thermographic patterns of normal canine stifles to the thermographic patterns of canine stifles post-cranial cruciate ligament repair, and to evaluate the thermographic changes occurring with the use of post-operative cryotherapy. By using thermography we will be able to comment on the effectiveness of using cryotherapy as a post-operative rehabilitation modality.

Background: Cranial cruciate ligament ruptures are one of the most common causes of hind limb lameness in dogs. Since the 1950s numerous techniques for surgical repair have been described in the literature. Surgical repairs can be divided into 2 categories of intra- versus extracapsular techniques, with the extracapsular lateral imbrication arguably being the most popular. Post-operative care remains quite contraversional, with cryotherapy (i.e. ice packing) and compression bandages remaining the mainstay of orthopedic rehabilitation modalities in an effort to relieve post-operative inflammation and pain. To date there is no data providing proof that this routine is efficacious.

Thermography is a noninvasive diagnostic imaging technique involving recording cutaneous thermal patterns. These patterns have been shown to directly correlate to various disease or injury as they relate to autonomic function. In human medicine thermography has been used for rheumatologic assessments, evaluation of vascular disorders, inflammatory diseases, and intervertebral disc disease. More recently thermography has been shown to provide a reliable and consistent evaluation of canine limbs and osteoarthritis1.

The inflammatory and osteoarthritic components of cranial cruciate ligament ruptures and the surgical repair may allow us to utilize thermography in an evaluation of the efficacy of cryotherapy as a post-operative rehabilitation modality.

Hypothesis: Thermographic patterns do change with cryotherapy post extracapsular lateral imbrication (ECLI) repair over a seven day period. Cryotherapy is an effective post-operative rehabilitation modality by reducing inflammation.

Material and Methodsi. Patient Parameters: Age, weight, sex, and breed of 10 dogs examined by an orthopedic

surgeon and diagnosed with cranial cruciate ruptures will be collected. ii. Experimental Design: ECLI repairs are to be performed in routine fashion in all dogs (n =

20). Using a stand mounted infrared camera with a focal plane array amorphous silicone microbolometer connected to a laptop computer for real-time analysis, images will be collected as per methods previously described1. Post-operative crypotherapy will be used on 10 dogs every 8 hours for 4 days. 10 dogs having surgery without cryotherapy will be used as controls. All dogs will be evaluated thermographically pre-operatively as well as at day 1, 3, 5, 7 and 14 days after surgery. Thermal images between groups will be compared. An additional comparison will be made to the thermographic patterns of healthy animal maps collected previously1.

Statistical Analysis: Statistically significant data correlations will be identified.

Research Facilities: The ECLI surgeries and thermographic mapping is to be performed at Long Island Veterinary Specialists.


37

Rationale: The rationale of this study is to identify the thermographic changes occurring with the use of cryotherapy and to comment on the efficacy of its use to reduce post-operative inflammation.

Significance: The significance of this study is that if cryotherapy can proven to be effective at reducing post-operative inflammation, it can be used to aid in and potentially hasten the recovery period post orthopedic procedures.

References: 1. Akgun K, Korpinar MA, Kalkan MT, Akarirmak U, Tuzun S, Tuzun F.Temperature changes in superficial and deep tissue layers with respect to time of cold gel pack application in dogs.

2. Barber FA.A comparison of crushed ice and continuous flow cold therapy.Am J Knee Surg. 2000 Spring;13(2):97-101; discussion 102.PMID: 11281337 [PubMed - indexed for MEDLINE]

3. Barry S, Wallace L, Lamb S.Cryotherapy after total knee replacement: a survey of current practice.Physiother Res Int. 2003;8(3):111-20.PMID: 14533367 [PubMed - indexed for MEDLINE]

4. Cina-Tschumi B. [Evidence-based impact of cryotherapy on postoperative pain, swelling, drainage and tolerance after orthopedic surgery]Pflege. 2007 Oct;20(5):258-67. Review. German. PMID: 18214217 [PubMed - indexed for MEDLINE]

5. Gibbons CE, Solan MC, Ricketts DM, Patterson M.Cryotherapy compared with Robert Jones bandage after total knee replacement: a prospective randomized trial.Int Orthop. 2001;25(4):250-2.PMID: 11561502 [PubMed - indexed for MEDLINE]

6. Levy AS, Marmar E.The role of cold compression dressings in the postoperative treatment of total knee arthroplasty.Clin Orthop Relat Res. 1993 Dec;(297):174-8.PMID: 7902225 [PubMed - indexed for MEDLINE]

7. Loughin CA, Marino DJ.Evaluation of thermographic imaging of the limbs of healthy dogs.Am J Vet Res. 2007 Oct;68(10):1064-9.

8. Shumway R.Rehabilitation in the first 48 hours after surgery.Clin Tech Small Anim Pract. 2007 Nov;22(4):166-70. Review.PMID: 18198785 [PubMed - indexed for MEDLINE]

9. Swenson C, Swärd L, Karlsson J.Cryotherapy in sports medicine.Scand J Med Sci Sports. 1996 Aug;6(4):193-200. Review.PMID: 8896090 [PubMed - indexed for MEDLINE]

10. Webb JM, Williams D, Ivory JP, Day S, Williamson DM.The use of cold compression dressings after total knee replacement: a randomized controlled trial.Orthopedics. 1998 Jan;21(1):59-61.

11. Yonsei Med J. 2004 Aug 31;45(4):711-8.


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http://proxy.library.upenn.edu:5567/pubmed/9474633?ordinalpos=6&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum








PMID: 15344214 [PubMed - indexed for MEDLINE]

12. Am J Phys Med Rehabil. 1991 Aug;70(4):181-5. The effect of ice on intra-articular temperature in the knee of the dog.


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