Automated Knee Image

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An automatic diagnosis method for the knee meniscus tearsin MR images

Cemal Ko se a, *, Okyay Genc alioglu b , Ugur Sevik a

a Department of Computer Engineering, Faculty of Engineering, Karadeniz Technical University, 61080 Trabzon, Turkeyb Department of Radiology, Faculty of Medicine, Karadeniz Technical University, 61080 Trabzon, Turkey

Abstract

Everyday vast amount of information accumulated in medical databases. These databases include quite useful information that couldbe exploited to improve diagnosis of illnesses and their treatments. However, classication of this information is becoming more andmore difficult. In this paper, an automatic method to diagnose the knee meniscus tears from MR medical images is presented. This pro-posed system uses histogram based method with edge detection ltering and statistical segmentation based methods to locate meniscus atknee joint. A template matching technique is also employed to extract the meniscus. Finally, the meniscus area is analyzed to detect themeniscus tears automatically. Accurate segmentation of the statistical pattern requires a technique that eliminates background effects.Hence, the density distributions of the statistical patterns on images with varying background are corrected. Here, the statistical segmen-tation method also extracts a representing image of the statistical patterns such as bone and uses the image to enhance the segmentation.Performance of this method is examined on MR images in varying qualities. The results show that our method is quite successful in seg-mentation of knee bones and diagnosis of the meniscus tears. This system has achieved accuracy about 93% in the diagnosis of meniscustears on MR images.

2007 Elsevier Ltd. All rights reserved.

Keywords: Medical image processing; Statistical image processing; Bone Segmentation; Knee meniscus location; Meniscus tears; Automatic diagnosis

1. Introduction

Medical databases contain vast amount of informationrelevant to illnesses and their cures. Users may spend alarge portion of their time to extract useful informationfrom the medical databases. In fact, many research projectshave been launched on data mining to extract information

from the medical databases. The developed systems areused to collect or classify the statistical informationobtained from the databases. In this study, a sample med-ical mining system running on large medical databases todetect knee meniscus tears is presented. Manual processingof these databases is quite tedious, costly and time consum-ing, since obtaining information from nation-wide medical

databases requires many users to spend excessive amountof time (Cicuttini, Wluka, Forbes, & Wolfe, 2003; DamErik, Folkesson, Pettersen Paola, & Christiansen, 2006;Inan, Firat, U cman, & Necaattin, 2004; Martinez, Sucar,& Acosta, 2006; Stammberger, Eckstein, Michaelis, Eng-lmeier, & Reiser, 1999). This proposed diagnosis systemenable users to collect valuable information easily from

these databases ( Diaz, Fdez-Riverola, & Glez-Pena, 2006;Hung & Chen, 2006; Latifoglu, Sahan, & Kara, 2007;Moon, Bullitt, van Leemput, & Gerig, 2002; Ryzewicz, Pet-erson, & Siparsky, 2007 ). Then, the medical researchersmay measure the effectiveness of the medical treatmentsconsidering the information in the databases and give someadvices to the doctors if necessary ( Guven & Kara, 2006;Kauffmann, Gravel, & Godbout, 2003; Ko ¨se, 2006).

Meniscus tears are very common knee joint diseaseresulting in cartilage and bone degeneration that causes painand loss of mobility. MR (magnetic resonance) imaging

0957-4174/$ - see front matter 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.eswa.2007.11.036

* Corresponding author. Tel.: +90 4623773167; fax: +90 4623257405.E-mail addresses: [email protected] (C. Kose), okyaygenc@meds.

ktu.edu.tr (O. Gencalioglu), [email protected] (U. Sevik).

www.elsevier.com/locate/eswa

Available online at www.sciencedirect.com

Expert Systems with Applications 36 (2009) 1208–1216

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allows 2D and 3D knee visualization and detection of menis-cus tears ( Martinez et al., 2006; Tamez-Pena, Barbu-McInn-sis, & Toterman, 2004 ). Processing many knee imagesmanually is quite laborious and can take signicant amountof time. Obtaining accurate resultsis also subject to the judg-ment of the clinician and requires a signicant experience

and training. Quantifying these problems will enable evalua-tion of therapies that may heal, slow down or stop degrada-tion in a knee. Consequently, there is a high demand toautomate the diagnosis and measurement processes ( Eriket al., 2006; Pyridonos, Papageorgiou, & Groumpos, 2006;Stammberger et al., 1999; Tamez-Pena et al., 2004 ). Then,a diagnosis system is implemented to detect knee meniscustears in a medical image.

Even though medical images give a picture of the smalldetails of the organs in the body, beneting from theseimages is quite difficult since data sets to be analyzed bytheradiologist is increasingsubstantially. In thisapplication,the scans obtained are grey scale images of knee consisting of 16 slices typically. All of these proton density weighted MRimages are acquired in sagittal plane ( Brix et al., 1990). Here,automatic organ segmentation and recognition approachescan be used to improve both the efficiency and the qualityof the diagnosis ( Bruijne & Nielsen, 2004; Gamanya, DeMaeyer, & De Dapper, 2007; Martinez et al., 2006; Pyrido-nos et al., 2006; Schilham, van Ginneken, & Loog, 2006 ;Tamez-Pena, Totterman, & Parker, 1999; Wu, Wang, &Ng, 2006). Hence, the automatic method using both statisti-cal andtemplate matching techniques is employed to analyzeand diagnose knee meniscus tears.

In this paper, several approaches are examined to locate

meniscal region at knee joints in order to automaticallydiagnose knee meniscus tears from sagittal MR images(Folkesson, Dam, Olsen, Pettersen, & Christiansen,2005). These are histogram based approaches with edgedetection option, and bone segmentation basedapproaches. To locate meniscal regions, these approachesare examined on images in varying qualities. After locatingthe meniscal region, a triangular template matchingmethod is employed to determine meniscuses at the knee joint (Alexander & Andriacchi, 2001 ). Then, the size of meniscus tears on each of the sagittal images is measuredfor the diagnosis. The system is also tested on images takenfrom different patients and the diagnosis performance of the system is measured for MR images in varying qualities.The rest of this paper is organized as follows. An overviewand the implementation details of the developed system aregiven in Section 2. Histogram and segmentation basedlocation approaches are also explained in the same section.A detailed description of the diagnosis system is presentedin Section 3. The results are discussed in Section 4. Theconclusion and future work are given in Section 5.

2. Methods for locating the meniscal region

In this section, histogram and statistical segmentation

based location methods are described in detail ( Folkesson

et al., 2005; Letteboer et al., 2004; Prastawa, Bullitt, &Ho, 2004; Tamez-Pena et al., 1999 ). In the application,the system rst locates the meniscal region on sagittalMR images and then applies a triangular template match-ing technique to diagnose the meniscus tears. On theproton density sagital knee MR images, meniscal areas at

knee joint are symmetrically formed triangular darkregions like horns so they are called anterior and posteriorhorns. In practice, physician rst concentrates on the knee joint and then searches the meniscus horns. Our system ismainly based on this simple idea.

Mainly two approaches are employed to locate meniscalregion. These are histogram based and statistical bone seg-mentation methods. Here, the correct determination of thelocation of the meniscal region depends on choosing thecorrect methods. In the literature, few other methods werealso proposed to locate meniscus regions (zones) ( Folkes-son et al., 2005; Song, Chi, & Wang, 2005; Villeger, Ouch-chane, & Lemaire, 2006 ).

2.1. The histogram based location methods

In this section, several methods to locate knee meniscalregions are introduced. Histogram based methods exploitthe morphology of the femur and the location of the fem-oral notch, so some knowledge of the knee anatomy is usedin the determination of the meniscus regions. Histogrambased methods generate vertical and horizontal histogramsof the knee images from the original MR scan. Then, his-tograms are smoothed, normalized and scaled down to tol-erate the rotations of the original image. Hence, vertical

and horizontal histograms are generated from each image.The maximum and minimum or both values of the histo-grams are used to determine the approximate locations of the meniscus horns.

Histogram based methods involve tree key steps thatcan be monitored by the user. In the rst step, verticaland horizontal histogram of the knee images are calcu-lated. Then, these histograms are compared to the refer-ence histograms to decide whether they are typical kneehistograms or not. If there is no matching between the ori-ginal histogram, scaled sequence of the original image withdifferent rotations are examined to calculate the angle of current rotation. Hence, the extreme values of the histo-gram are used to locate the knee joints as given in Fig. 1.Here, the cartilage regions generally have higher intensityvalues compared to the surrounding bones/tissues at knee joints. Therefore, the cartilage regions can be chosen as ref-erence and these high intensity regions can be used to deter-mine the exact positions of the meniscal region.

In the application, several histogram based methods areemployed. These are histogram based method, and normal-ized histogram based method with edge detection or lter-ing. The histogram based method just generates the verticaland horizontal smoothed histograms of the images andthen searches the extremes for locating the meniscal region

as illustrated in Fig. 1. The second method rst lters the

C. Kose et al. / Expert Systems with Applications 36 (2009) 1208–1216 1209



image by using lters such as Sobel, Prewitt or Robertslter, and then generates normalized histograms ( Sonkaet al., 1998). Here, the vertical and horizontal histogramsare normalized by considering only the pixels accountedin the histogram calculation. Therefore, pixels under a cer-tain threshold intensity values are not taken into consider-

ation and the vertical and horizontal histograms arenormalized related to the number of pixel used in the cal-culation. Finally, the extremes of the normalized histogramare used to localize the meniscal region as shown in Fig. 2.This gure shows that the horizontal location was foundsuccessfully but vertical location was not found that muchprecisely.

2.2. The segmentation based location method

As it can be seen from previous results, the meniscalregion can be located in large intervals vertically. Thisdecreases the accuracy of the diagnosis and increases thecomputation time of the process. Thus, the anatomy of the knee and bones can be also be used in locating the men-iscal region in order to reduce computation time andincrease the accuracy. Then, a method, using knowledgeof the knee anatomy, exploits the morphology of the femurand the location of the femoral notch and locates theregion. Here, a statistical method is employed to performsegmentation of knee bones as presented in Fig. 3. Theknee bone is rst segmented automatically, and then thehistograms of the bones are used to localize the meniscusregions on each slice of knee images.

In the application, vertical and horizontal histograms of

the segmented bones are rst determined to locate the men-

iscal regions. In the next step, the boundaries and the mid-dle of the meniscus regions are detected. The basic idea isto exploit the characteristic shape of the knee bone slicesand meniscus regions and the intensity values of the sur-rounding regions as shown in Fig. 3. The essential stepsof this statistical method are determination of characteris-tic images (CIs), obtaining statistical properties of the

statistical texture or bone, background toleration and

Fig. 1. Locating the meniscal region by using the histogram basedmethod.

Fig. 2. Locating the meniscal region by using the normalized histogrambased method with ltering.

Fig. 3. Locating the meniscal region by using the statistical segmentationmethod.

1210 C. Kose et al. / Expert Systems with Applications 36 (2009) 1208–1216





statistical properties of a texture and performs segmenta-tion of bones in a knee slice. The method compares the sta-tistical properties of the reference CI and sample image. If the difference is under a certain threshold, the pixel is seg-mented as bone and this process carried out on all the otherpixels on the image. After calculating the intensity distribu-tions of each sample at the current pixel, they are normal-ized and compared with the chosen CIs’ distributions.Then, differences between intensity distributions are calcu-lated by using the Eq. (3). Finally, the segmented bones(femur and tibia) are enlarged by employing a simple adap-tive region growing technique based on average intensity(Pohle & Toennies, 2001 ). A sample segmented image is

given in Fig. 6.

f Dist Err gl ¼ Xb

i¼a

jf½ Int Dist ðiÞCI l ½ Int Dist ðiÞSample l gj:

ð3Þ

where f Dist Err gl is the difference between the intensity dis-tributions of CI and current sample image in size l, Int Dist ðÞCI is the intensity distributions of CI and Int Dist ðÞSample is the intensity distribution of the currentsample image. Here, i is the intensity index and varies froma to b. a (a P 0) is the smallest and b (b 6 255) is the great-est common intensity index of both distributions.

3. Diagnosing meniscus tears

The diagnosis system for meniscus tears is described inthe following owchart in Fig. 7. The basic steps in thisautomatic diagnosis of meniscus tears are (1) calculatingthe histograms, or obtaining the statistical properties of the bone slices at knee and applying the statistical segmen-tation method to segment the bones, (2) locating the men-iscal regions, (3) exposing details of the localized image byusing histogram equalization technique and then generat-ing the binary image by employing Otsu adaptive threshold

algorithm ( Otsu, 1979; Walker, Wolfart, Fisher, & Perkins,

1994), (4) applying the template matching method in orderto determine meniscus horns’ areas, (5) measuring the men-iscal tears, (6) evaluating the results, and nally (7) diag-nosing the meniscus tears.

3.1. A template matching technique for detecting meniscuses

The morphological and geometric structures of the men-iscal regions are exploited to detect the meniscus horns.Several polygonal (triangular) templates in various sizesare used for detecting the regions. The optimum matchingtriangle is searched to estimate the meniscal area on eachimage slice with meniscus. Here, the triangle consists of ablack triangle in the middle and this black triangle is sur-rounded by a thick white triangular polygon as shown inFig. 8. The black area corresponds to the meniscus hornand the white area corresponds to the surrounding regionsof the meniscus horns. Template matching is only done inthe previously determined meniscal region.

In our application, statistical information about themeniscuses are obtained by using a large number of sam-ples and it is seen that the meniscus horns may be repre-sented in triangular forms in various sizes. Then, thetriangle matching is searched within the location limitedvertically and horizontally based on the statistical informa-tion. Finally, the best matching triangle in terms of size andposition is found to determine the size of the meniscushorns. Here, the relative position and sizes of the meniscushorns are also considered. A localized image of an originalMR image is shown in Fig. 8a. The details of the localizedimage are exposed by using histogram equalization tech-

nique as given in Fig. 8b. Then, Otsu adaptive threshold

Fig. 6. Locating the meniscal region by the statistical segmentationmethod.

Start

Calculate histograms

Knowledge base Locate meniscal region

Segment knee bones

Apply the template matching

Obtain statistical properties

Manual measurements

Measure tears

Evaluate results

Diagnose meniscus tears

Apply histogram equalization,Generate binary image

Detect meniscus horns

Fig. 7. A ow chart of meniscus tears diagnosis.




algorithm is employed to generate the binary image asshown Fig. 8c. The determination of the meniscus regionsis shown in Fig. 8d and e. Here, as it can be seen, the menis-cus horns are clearly determined.

In a large proportion of the MR scans, the meniscushorns are shaped as a triangular with low pixel intensity,surrounded by cartilage/tissues with high pixel intensity.The edges of the meniscus region are found by detectingrapidly changing areas in terms of intensity because theregion is mostly surrounded by cartilage. Practical experi-ments also show that the intensity of the meniscal tears islower than the intensity of the surrounding cartilages andthis is quite useful in determination of the meniscus horn.Here, the template matching exploits the characteristicshape of the meniscal regions and the intensity values of the surrounding regions. The template matching techniqueusing triangles in varying sizes is employed to determinethe rough boundaries of the meniscus regions. Finally,the best matching triangle is used to detect exact locationof the meniscuses.

3.2. Measuring the meniscus tears

Automatic measurement of meniscus tears is preferablebecause it avoids inter/intra-observer variability and it isbetter for clinical studies and diagnosis. The areas of themeniscus tears are calculated by searching the high inten-sity pixels within the black triangle. Here, low intensity pix-els surrounded by the high intensity cartilage/tissue pixelare considered in the determination of meniscus tear ratio.Areas of meniscus region of each slice are calculated bysumming each corresponding pixel in all meniscus regions.The real corresponding dimensions of pixels (the width andheight of real area) are obtained from the metadata in theMR images. Hence, the system calculates the area of themeniscuses and tears. If the area of the tears is larger thana threshold value (an experimentally determined value),

meniscus tears are diagnosed as positive. Hence, medical

researchers may take the patient into further examinationand applies proper medical treatment plan.

4. Results

In this paper, an automatic system is presented to diag-nose knee meniscus tears. This system uses a simple histo-gram based method and statistical segmentation basedmethods to locate meniscal region at a knee. Besides, thesystem employs a triangular template matching methodto diagnose knee tears on MR images. In this study, aboutve hundred images collected from 30 patients at the

Department of Radiology at Faculty of Medicine atKaradeniz Technical University and processed for thediagnosis. The system was evaluated on these various qual-ity medical images and results show that the system quitesuccessfully locates the meniscal region.

In order to examine the system, a hundred 8 bit grey-scale test images taken from 24 different patients are chosenfrom our library of MR images. The diagnosis system wasrun on PC with P4-3.2 GHz CPU and 512 MB RAM. Forthe diagnosis, all of these test images are processed in3.92 min on the system. This result proves that our systemis quite promising for large-scale mining applications.

A common problem encountered during segmentation isthe non-uniformity of the bone textures. This is due to het-erogeneous properties of bones and some abnormalities. Inmost cases, the segmentation method works well andlocates the meniscal region correctly. The number of images where incorrect segmentation occurs for a patientknee also depends on the set up of the MR scanner.

Evaluation of the results show that histogram basedmethod achieves 39.0% performance in locating the meni-scal region horizontally. On the other hand, if the mini-mum value between two maximums on the verticalhistogram is chosen as the reference point, the methodachieves about 95.0% of accuracy in locating the meniscal

region vertically. Here, if the maximums are chosen as

Fig. 8. Triangle matching and extraction of the meniscus horns.




the reference points, the system achieves 99.0% of accuracyin locating the meniscal region vertically. In general, thishistogram based method achieves 38.0% of accuracy inlocating the knee meniscal regions.

Another approach is applying an edge detection lteringto whole image, and then applying normalized histogram

based method to the ltered image. This approach espe-cially increases the performance of nding horizontal loca-tion signicantly. The horizontal performances of thesystem are given in Table 1 . As illustrated in Fig. 9, withthis improvement general performance of the system isenhanced 60.0% and it reached to 98.0% at locating meni-scal area. On the other hand, these ltering approaches donot enhance the vertical performance of the system. On thecontrary, they dramatically improve the horizontal perfor-mance of system.

Experiments show that 11.0% of the vertical intervalsare determined as expected. In others words, 89.0% of the vertical intervals are over estimated. Although the per-formance of the system at locating the regions is quitegood, this over estimation increases the processing timeabout 20% during template matching and diagnosis, anddecreases the accuracy in diagnosis. Therefore, more pre-

cise locating technique is needed to reduce the processingtime and to increase the accuracy of the system.

The proposed statistical bone segmentation technique isemployed to locate the medical regions more preciselybecause the most known invariant morphology is the struc-ture of the bones (femur and tibia) at a knee. Therefore,

bone segmentation may help to gure out the morpholog-ical structure of the knee joint more precisely so that it canbe used to localize the meniscal region more accurately.After the segmentation, the circular ltering technique isused to remove small segmented fragments on the seg-mented image ( Ko se, 2006). Then, the resulting image isrotated to tolerate the rotation and histograms of therotated image are compared with the histograms of the ref-erence images to determine exact orientation of the inputimage. Hence, the simple statistical bone segmentationtechnique with the region growing method is employedand the results show that 82.0% of meniscal regions arelocated accurately. On the other hand, if only the femuris considered, the performance goes further up to 94.0%and the rest 6.0% of meniscal regions is also located in largeinterval vertically.

Finally, the diagnosis system is tested on various quali-ties of knee MR images. Here, the triangular templatematching technique is used to detect the knee meniscushorns. Hence, 93.5% of the meniscus horns on two-dimen-sional images are detected by using template matchingtechnique. Here, experimentally determined triangular tem-plates are rst used to search the meniscus horns and then

Table 1Horizontal localization performances after ltering

Sobel Prewitt Roberts Robinson Frei-Chen

Normal 98% 98% 96.0% 98% 98%Normalized 100% 100% 96.0% 99% 100%

Fig. 9. A typical location of meniscal region. Fig. 10. Measuring the meniscus tears.




an optimum triangular template is searched for each men-iscal area. Results are also examined by medical research-ers. These results show that 88.3% of 30 knee imageswith tears are successfully diagnosed. On the other hand,95.7% of the 70 knee images without knee tears are diag-nosed accurately. In other words, only thirteen out of

two hundred meniscus horns are not correctly detectedbecause of poor quality and noise. In addition to these, aquantitative method is also applied to measure the amountof tears with 10% tolerance. These results also show thatover 90.0% of measurements are quite successful and othermeasurements are less precise than expected. The auto-matic meniscus tear measurements are compared with themanually measured results. These results show that the sys-tem successfully detects and diagnose about 90.0% of themeniscus tears from MR images. Fig. 10 shows a typicaldiagnosis and measurement of the meniscus tears.

5. Conclusion and future work

In this study, our goal is to automatically diagnose themeniscus tears on knee MR images. In order to do this,we addressed simple histogram based, statistical segmenta-tion methods and triangular template matching technique.Here, the histogram and statistical techniques are used tolocate meniscal region and the triangular template match-ing technique is used to detect meniscus tears for diagnosis.The diagnosis system achieves accuracy over 93.0% in diag-nosis of knee meniscus tears on various qualities of MRimages.

Although some satisfactory results are obtained, the

diagnosis system still needs to be improved. The resultsof the experiments show that the methods proposed forknee meniscus tear diagnosis may also be applied to theother areas of medical image analysis. So, another futuretask is to apply this proposed method to other areas inmedical image analysis.

This automatic diagnosis method is developed to classifythe images of large medical databases. Here, this simpleand fast diagnosis method may be used to extract informa-tion from the large medical databases. An important futurework would be measuring the performance of this methodin mining medical databases.

Acknowledgements

We would like to thank to staff and faculty members inFaculty of Medicine at Karadeniz Technical University forproviding the MR image data sets used in this study. Theauthors would also like to thank to Prof. Dr. Ahmet SARIfor his contribution on the medical issues in this study.

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Automated Knee Image