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ABSTRACT

Tuberculosis (TB) is a common disease with high mortality and morbidity rates

worldwide. The chest radiograph (CXR) is frequently used in diagnostic algorithms for

pulmonary TB. Automatic systems to detect TB on CXRs can improe the ef!ciency of such

diagnostic algorithms. The dierse manifestation of TB on CXRs from different populations

requires a system that can be adapted to deal with different types of abnormalities.

A computer aided detection (CA") system was deeloped which combines the results of

superised subsystems detecting te#tural$ shape$ and focal abnormalities into one TB score. The

te#tural abnormality subsystem proided seeral subscores analy%ing different types of te#tural

abnormalities and different regions in the lung. The shape and focal abnormality subsystem each

proided one subscore. A general framewor& was deeloped to combine an arbitrary number of

subscores' subscores were normali%ed$ collected in a feature ector and then combined using a

superised classi!er into one combined TB score.

Two databases$ both consisting of digital CXRs$ were used for ealuation$ acquired

from (A) a *estern high+ris& group screening and (B) TB suspect screening in Africa. The

subscores and combined TB score were compared to two references' an e#ternal$ non+

radiological$ reference and a radiological reference determined by a human e#pert. The area

under the Receier ,perator Characteristic (R,C) cure.

The combined TB score performed better than the indiidual subscores and approaches performance of human obserers with respect to the e#ternal and radiological reference.

-uperised combination to compute an oerall TB score allows for a necessary adaptation of the

CA" system to different settings or different operational requirements.

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CHAPTER 1

INTRODUCTION

mage processing operations can be roughly diided into three ma/or categories$

mage Compression$ mage 0nhancement and Restoration$ and 1easurement 0#traction.

t inoles reducing the amount of memory needed to store a digital image. mage

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defects which could be caused by the digiti%ation process or by faults in the imaging set+

up (for e#ample$ bad lighting) can be corrected using mage 0nhancement techniques.

,nce the image is in good condition$ the 1easurement 0#traction operations can be used

to obtain useful information from the image. The mage 0nhancement and 1easurement0#traction are used to 23 grey+scale images. This means that each pi#el in the image is

stored as a number between to 22$ where represents a blac& pi#el$ 22 represents a

white pi#el and alues in+between represent shades of grey. These operations can be

e#tended to operate on colour images.

1.1 Introduction to Image Processing

mage processing is a method to conert an image into digital form and perform

some operations on it$ in order to get an enhanced image or to e#tract some useful

information from it. t is a type of signal dispensation in which input is image$ li&e ideo

frame or photograph and output may be image or characteristics associated with that

image. 4sually mage 5rocessing system includes treating images as two dimensional

signals while applying already set signal processing methods to them. mage processing

basically includes the following three steps.

• mporting the image with optical scanner or by digital photography.

• Analy%ing and manipulating the image which includes data compression and

image enhancement and spotting patterns that are not to human eyes li&e satellite

photographs.

• ,utput is the last stage in which result can be altered image or report that is based

on image analysis.

1.1.1 Purpose o Image processing

• The purpose of image processing is diided into 2 groups. They are'

• 6isuali%ation + ,bsere the ob/ects that are not isible.

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• mage sharpening and restoration + To create a better image.

• mage retrieal + -ee& for the image of interest.

• 1easurement of pattern 7 1easures arious ob/ects in an image.

• mage Recognition 7 "istinguish the ob/ects in an image.

1.1.! T"pes

The two types of methods used for mage 5rocessing that isAnalog and "igital

mage 5rocessing. Analog or isual techniques of image processing can be used for the

hard copies li&e printouts and photographs. mage analysts use arious fundamentals of

interpretation while using these isual techniques. The image processing is not /ust

confined to area that has to be studied but on &nowledge of analyst. Association is

another important tool in image processing through isual techniques. -o analysts apply a

combination of personal &nowledge and collateral data to image processing.

"igital 5rocessing techniques help in manipulation of the digital images by using

computers. As raw data from imaging sensors from satellite platform contains

deficiencies. To get oer such flaws and to get originality of information$ it has to

undergo arious phases of processing. The three general phases that all types of data hae

to undergo while using digital technique are 5re+ processing$ enhancement and display$

information e#traction.

There are two general groups of 8images9' ector graphics or line art and bitmaps

pi#el+based or 8images9. -ome of the most common file formats are'

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:; +An bit destructiely compressed bitmap format. This format is commonlyused on the nternet. This format limits the number of colour tones possible

in the photo to 23. t is frequently used for logos$ icons or blac& and white

photos and the quality is lower.

T;; +The standard > bit publication bitmap format.t is used for high+quality

photos. t is used for scanners$ digital cameras and printers.:ienthe

superior quality of the image$ the file si%e is also ery large.

5- +5ostscript$ a standard ector format. ?as numerous sub+standards andan be

difficult to transport across platforms and operating systems.

5-" +A dedicated 5hotoshop format that &eeps all the information in animage

including all the layers.

5ictures are the most common and conenient means of coneying or transmitting

information. A picture is worth a thousand words. 5ictures concisely coney information

about positions$ si%es and inter relationships between ob/ects. They portray spatial

information that we can recogni%e as ob/ects. ?uman beings are good at deriing

information from such images$ because of our innate isual and mental abilities. About

@2 of the information receied by human is in pictorial form. An image is digiti%ed to

conert it to a form which can be stored in a computers memory or on some form of

storage media such as a hard dis& or C"+R,1. This digiti%ation procedure can be done

by a scanner$ or by a ideo camera connected to a frame grabber board in a computer.

,nce the image has been digiti%ed$ it can be operated upon by arious image processing

operations.

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1.1.# R$B Co%or

The R:B color model is an additie color model in which red$ green$ and blue

light are added together in arious ways to reproduce a broad array of colors. R:B uses

additie color mi#ing and is the basic color model used in teleision or any other medium

that pro/ects color with light. t is the basic color model used in computers and for web

graphics$ but it cannot be used for print production.The secondary colors of R:B is cyan$

magenta$ and yellow are formed by mi#ing two of the primary colors (red$ green or blue)

and e#cluding the third color. Red and green combine to ma&e yellow$ green and blue to

ma&e cyan$ and blue and red form magenta. The combination of red$ green$ and blue in

full intensity ma&es white.

1.! App%ications

mage processing has an enormous range of applications almost eery area of

science and technology can ma&e use of image processing methods. ?ere is a short list

/ust to gie some indication of the range of image processing applications.

1edicine

• nspection and interpretation of images obtained from X+rays$ 1R

or CAT scans$

• Analysis of cell images.

Agriculture

• -atelliteDaerial iews of land$ for e#ample to determine how much

land is being used for different purposes$ or to inestigate the

suitability of different regions for different crops$

• nspection of fruit and egetables distinguishing good and fresh

produce from old.

ndustry

• Automatic inspection of items on a production line$

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• nspection of paper samples.

Eaw enforcement

• ;ingerprint analysis$

• -harpening or de+blurring of speed+camera images.

1.#Aspects o image processing

t is conenient to subdiide different image processing algorithms into broad

subclasses. There are different algorithms for different tas&s and problems$ and often

would li&e to distinguish the nature of the tas& at hand.

1.#.1 Image En&ancement

This is refers to processing an image and the result is more suitable for a particular

application. 0#amples include

• sharpening or de+blurring an out of focus image$

• highlighting edges$

• improing image contrast$ or brightening an image$

• Remoing noise.

1.#.! Image Restoration

This may be considered as reersing the damage done to an image by a &nown

cause$ for e#ample

• remoing of blur caused by linear motion$

• remoal of optical distortions$

• Remoing periodic interference.

1.#.# Image Segmentation

This inoles subdiiding an image into constituent parts$ or isolating certain

aspects of an image.

• circles$ or particular shapes in an image$

• n an aerial photograph$ identifying cars$ trees$ buildings$ or roads.

These classes are not dis/oint a gien algorithm may be used for both image

enhancement or for image restoration.

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1.'.Ac(uiring t&e image'

A digital image can be done using either a CC" camera$ or a scanner.

1.'.1 Preprocessing

This is the step ta&en before the ma/or image processing tas&. The problem here is

to perform some basic tas&s in order to render the resulting image more suitable for the

/ob to follow. n this case it may inole enhancing the contrast$ remoing noise$ or

identifying regions li&ely to contain the postcode.

1.'.! Segmentation

-egmentation actually get the postcode$ in other words to e#tract from the imagethat part of it which contains /ust the postcode.

1.'.# Representation and description

These terms refer to e#tracting the particular features which allow us to

differentiate between ob/ects that is cures$ holes and corners which allow us to

distinguish the different digits which constitute a postcode.

1.'.' Recognition and interpretation

This means assigning labels to ob/ects based on their descriptors (from the

preious step)$ and assigning meanings to those labels. Then identify particular digits$

and we interpret a string of four digits at the end of the address as the postcode.

1.'.)Image processing tec&ni(ues

mage processing is any form of signal processing for which the input is an image$

such as a photograph or ideo frame. The output of image processing may be either animage or a set of characteristics or parameters related to the image. 1ost image+

processing techniques inole treating the image as a two+dimensional signal and

applying standard signal+processing techniques to it. mage processing usually refers to

digital image processing$ but optical and analog image processing also are possible.

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mage processing is closely related to computer graphics and computer ision. mage

processing is a method to conert an image into digital form and perform some

operations on it$ in order to get an enhanced image or to e#tract some useful information

from it. t is a type of signal dispensation in which input is image$ li&e ideo frame or photograph and output may be image or characteristics associated with that image.

4sually mage 5rocessing system includes treating images as two dimensional signals

while applying already set signal processing methods to them.

1.) Digita% Image Processing

mage 5rocessing Toolbo# proides a comprehensie set of reference+standard

algorithms$ functions$ and apps for image processing$ analysis$ isuali%ation$ andalgorithm deelopment. t can perform image analysis$ image segmentation$ image

enhancement$ noise reduction$ geometric transformations$ and image registration. 1any

toolbo# functions support multicore processors$ :54s$ and C+code generation. mage

5rocessing Toolbo# supports a dierse set of image types$ including high dynamic range$

gigapi#el resolution$ embedded CC profile$ and tomography. 6isuali%ation functions and

apps let you e#plore images and ideos$ e#amine a region of pi#els$ ad/ust color and

contrast$ create contours or histograms$ and manipulate regions of interest (R,s). The

toolbo# supports wor&flows for processing$ displaying$ and naigating large images.

As a fundamental problem in the field of imageprocessing$ image restoration has

been e#tensiely studiedin the past two decades. t aims to reconstructthe original high+

quality image # from its degraded obseredersion y$ which is a typical ill+posed linear

inerse problem.

Classical regulari%ation terms utili%e local structural patternsand are built on the

assumption that images are locallysmooth e#cept at the edges. -ome representatie wor&s

in theliterature are the total ariation (T6)$ half quadratureformulation$ and 1umford+

-hah (1-) models. These regulari%ation terms demonstrate high effectieness

inpresering edges and recoering smooth regions. ?oweer$they usually smear out

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image details and cannot deal wellwith fine structures$ since they only e#ploit local

statistics$neglecting nonlocal statistics of images.

CHAPTER*!

PROB+E, IDENTI-ICATION

• A -61 is a binary classifier$ that is$ the class labels can only ta&e two alues'

FG.

• Cannot predict multiple result with -61 Binary classifier.

• This binary classification can classify only normal and abnormal type.

• Hot able to classify multiple stage with this classifier.

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CHAPTER #

+ITERATURE REIE/

#.1 Introduction

Caitation at the lung parenchyma is a hallmar& sign of tuberculosis$ a common

deadly infectious disease. t is de!ned as a gas !lled space within a pulmonary

consolidation$ a mass$ or a nodule$ produced by the e#pulsion of the necrotic part of the

lesion ia the bronchial tree. Caities can also occur in diseases such as primary

bronchogenic carcinoma$ lung cancer$ pulmonary metastasis and other infections.

Caities are quite isible and distinct in CT images but are often barely isible in chest

radiographs due to other superimposed I" lung structures in the " pro/ection image. n

chest radiographs$ the appearance of caities is ha%y$ and the caity walls are often ill+

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de!ned or completely inisible .This poses a big problem for radiologists to detect and

accurately segment caities in chest radiographs.

A dynamic programming based approach for caity border segmentation. The center of

the caity is ta&en as an input to de!ne the region of interest for dynamic programming.

A pi#el classi!er is trained to discriminate between caity borders and normal lung pi#els

using te#ture$ ?essian and location based features constructing a caity li&elihood map.

This li&elihood map is then used as a cost function in polar space to !nd optimal path

along the caity border. The proposed technique is tested on a large caity dataset and

=accard oerlapping measure is used to calculate the segmentation accuracy of our

system.

#.! SE$,ENTATION

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mage segmentation refers to the process of partitioning a digital image into

multiple segments i.e. set of pi#els$ pi#els in a region are similar according to some

homogeneity criteria such as colour$ intensity or te#ture$ so as to locate and identify

ob/ects and boundaries in an image .n 5ractical application of image segmentation rangefrom filtering of noisy images$ medical applications (Eocate tumors and other

pathologies$ 1easure tissue olumes$ Computer guided surgery$ "iagnosis$

Treatmentplanning$ study of anatomical structure)$ Eocate ob/ects in satellite images

(roads$ forests$ etc.)$ ;ace Recognition$ ;inger print Recognition$ etc.

#.!.1 Ca0it" segmentation

A noel technique to automatically segment caities based on dynamic programming

which uses the li&elihood map output of pi#el classi!er as cost function. *e hae

alidated our results with those obtained by three human e#pert readers on a large dataset

including prominent as well as subtle caities. ,ur results are ery encouraging and

comparable with the degree of oerlap between trained human readers and a chest

radiologist. The accuracy of our technique for difficult caities can be increased by

improing the pi#el classi!er and optimi%ing the parameters for dynamic programming. t

may be possible to deelop pi#el based features more speci!c to caity borders so as to

diff erentiate it with ribs and other bone structures. -uch a tool could be ery helpful in

treatment monitoring for tuberculosis.

#.# Re0ie on Paper

An improed Juid ector Jow for caity segmentation in chest radiographs year

(G) .Xu$ T.$ Cheng$ . present the tuberculosis detection. Assessing the si%e of caity

and its ariation between temporal scans is important for disease diagnosis and to

measure the response to therapy. -tudies hae shown the e#istence of caitation in

postprimary tuberculosis (TB) which is een higher in TB patients haing diabetes . The

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number and the si%e of caities is a ital element in tuberculosis scoring systems for chest

radiographs. -mall agreement (.22 &appa statistic) has been reported on detection of

caities in 23 chest radiographs obtained from a TB screening database .Automated

detection and segmentation of caities is a less e#plored research area. proposed adetection system for caities in chest radiographs for screening of TB. Their system is

based on a superised learning approach in

which candidates are segmented using a mean shift segmentation technique with adaptie

thresholding for initial contour placement followed by segmentation using a sna&e model.

-egmented candidates are then classified as caity or noncaity candidate using Bayesian

classifier trained on gradient inerse coefficient of ariation and circularity measure

features. The technique was tested on only G3 caity chest radiographs. Threshold on

Tanimoto oerlapping measure has been used to classify detected caity regions as true or

false posities. The accuracy of contour segmentation of caities has not been mentioned

in the wor& . proposed caity segmentation based on an improed edge+based fluid ector

flow sna&e model. This was alidated on chest radiographs and resulted in a =accard

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pro/ection of the lung fields and the mediastinum. The lateral parts at the acromial end

outside the lung fields are not consider. ,btaining an accurate segmentation of the

claicles is useful for a number of applications. The segmentation can be used to digitally

subtract the claicle from the radiograph. Accurate locali%ation of the medial parts of the

claicles can also sere to automatically determine possible rotation of the ribcage$ an

important quality aspect of chest radiographs. *hen chest radiographs are rotated$ false

abnormalities might appear in either or both of the lung fields due to apparent changes in

parenchymal density.

n the year of GG stefan /aeger et.al K>L presentthe detection of TB and other

diseases in CXRs as a pattern+recognition problem. The algorithms are deeloped by

using #+rays from the =apanese -ociety of Radiology Technology database. The

preprocessing step first enhanced the contrast of the image using a histogram equali%ation

technique. He#t step include lung field e#traction from the other structures in the #+

raysuch as the heart$ claicles$ and ribsbased on an adaptie segmentation method.

"eiations from the lung shape and increased lung opacity indicate abnormalities$ such

as consolidations or nodules. These abnormalities with a bag+of+features approach that

included descriptors for shape and te#ture. To detect nodules$ for e#ample first applied a

:aussian filter and computed the 0igen alues of the ?essian matri#. Then computed a

multi+scale similarity measure that responds to spherical 8blobs9 with high

curature.;inally these features are used to train a binary classifier that discriminates

between normal and abnormal CXRs. The implementation of a preliminary system that is

capable of detecting some manifestations of disease in CXRs. Hoel algorithms can be

implemented on any portable #+ray unit.

n the year of bram an ginne&enet.al K2Lpresenta fully automatic method is

presented to detect abnormalities in frontal chest radiographs which are aggregatedinto an

oerall abnormality score. The method is aimed at finding abnormal signs of a diffuse

te#tural nature$ such as they are encountered in mass chest screening against tuberculosis

(TB).The scheme starts with automatic segmentation of the lungfields$ using actie shape

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models. The segmentation is used tosubdiide the lung fields into oerlapping regions of

arioussi%es. Te#ture features are e#tracted from each region$ usingthe moments of

responses to a multiscale filter ban&. TheMdifference featuresN are obtained by subtracting

feature ectorsfrom corresponding regions in the left and right lung fields. Aseparatetraining set is constructed for each region. All regionsare classified by oting among the

nearest neighbors$ withleae+one+out. He#t$ the classification results of each region

arecombined$ using a weighted multiplier in which regions withhigher classification

reliability weigh more heaily. This produces an abnormality score for each image. The

method is ealuated ontwo databases. The first database was collected from a TB

masschest screening program$ from which G>@ images with te#turalabnormalities and >G

normal images were selected. Although thisdatabase contains many subtle abnormalities$

the classificationhas a sensitiity of .


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n the year of GI ?.B Rachanaet.alK@Lpresent TB detection is based on sputum

e#amination microscopically by using Piehl+ Heelsen stain (PH+stain) method.The

deeloped algorithm detects the TB bacilli automatically. This automated system reduces

fatigue by proiding images on the screen and aoiding isual inspection of microscopicimages. The system has a high degree of accuracy$ specificity and better speed in

detecting TB bacilli. The method is simple and ine#pensie for use in ruralDremote areas

in the emerging economies. -egmentation algorithm is deeloped to automate the process

of detection of TB using digital microscopic images of different sub/ects. A performance

comparison of clustering and thresholding algorithms for segmenting TB bacilli in PH+

stained tissue slide images is carried out. The results presented showed that a more

conincing segmentation performance has been achieed by using the clustering

methods$ as compared to the thresholding method. These results also suggest that &+mean

clustering is the best method for segmenting the bacilli$ as it is highly sensitie to the TB

pi#els.

n the year of bram an ginne&enet.al K


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adaptie lung models that detects lung boundaries$ surpassing state+of+the+art

performance. The method consists of three main stages' (i) acontent+based image

retrieal approach for identifying training images with mas&s most similar to the patient

CXR using a partial Radon transform and Bhattacharyya shape similarity measure$ (ii)creating the initial patient+specific anatomical model of lung shape using -;T+flow for

deformable registration of training mas&s to the patient CXR$ and (iii) e#tracting refined

lung boundaries using a caity segmentation optimi%ation approach with a customi%ed

energy function.

n the year of Bram an :inne&enet.alKGL presents an actie shape model

segmentation scheme is presented that is steered by optimal local features$ contrary to

normali%ed first order deriatie profiles$ as in the original formulation.A nonlinearQHH+

classifier is used$ instead of the linear 1ahalanobis distance$ to findoptimal

displacements for landmar&s. ;or each of the landmar&sthat describe the shape$ at each

resolution leel ta&en into accountduring the segmentation optimi%ation procedure$ a

distinct set ofoptimal features is determined. The selection of features is automatic$ using

the training images and sequential feature forwardand bac&ward selection. The new

approach is tested on syntheticdata and in four medical segmentation tas&s' segmenting

the rightand left lung fields in a database of I chest radiographs$ and segmenting the

cerebellum and corpus call sum in a database of Oslices from 1R brain images. n all

cases$ the new method produces significantly better results in terms of an oerlap error

measure (Gusing a paired T+test) than the original actieshape model scheme.

n the year of GG?aithemBoussaid et.alKGGL present a machine learning approach

to improe shape detection accuracy in medical images with deformable contour models

("C1s)."C1s can efficiently recoer globally optimal solutions that ta&e into account

constraints on shape and appearance in the model fitting criterion. This model can also

deal with global scale ariations by operating in a multi+scale pyramid. The main

contribution consists in formulating the tas& of learning the "C1 score function as a

large+margin structured prediction problem. The algorithm trains "C1s in an /oint

manner all the parameters are learned simultaneously$ while use rich local features for

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landmar& locali%ation. Then ealuate a method on lung field$ heart$ and claicle

segmentation tas&s using >@ standard posterior+anterior (5A) chest radiographs from the

-egmentation in Chest Radiographs (-CR) benchmar&. "C1s systematically outperform

the state of the art methods according to a host of alidation measures including theoerlap coefficient$ mean contour distance and pi#el error rate.

CHAPTER '

PROPOSED S2STE,

Tuberculosis is a ma/or health threat in many regions of the world. "iagnosing

tuberculosis still remains a challenge. *hen left undiagnosed and thus untreated$

mortality rates of patients with tuberculosis are high. -tandard diagnostics still rely on

methods deeloped in the last century. An automated approach for detecting tuberculosis

in conentional poster anterior chest radiographs. ;irst to remoe the noise from the

images. ;or filtering the images we use the wiener filter for diagnosing. n a second step

use caity segmentation approach and model the lung boundary detection with an

ob/ectie function. caity segmentation is applied specifically to those models which

perform a ma#+flowDmin+cut optimi%ation. After lung segmentation we e#tract three

features such as EB5$ ?,:$ and ?0 features are e#tracted. Then classified the image

using binary classifier.

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'.1 ,odu%es

• 5reprocessing

• Caity -egmentation

•

EB5 feature 0#traction• ?,: feature 0#traction

• ?0(?essian mage 0nhancement) feature 0#traction

• -61 classifier

20

nput mages

5reprocessing

Caity -egmentation

;eature 0#traction

EB5 ;eatures ?,: ;eatures ?0 ;eatures

-61 classifier "atabase

Result


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;igure' >.G system architecture

The figure >.G shows$first gie the input image$then the input image moe from

the preprocessing step. n this preprocessing step to remoe the noise from the

image.after that it sends graph cut segmentation.By using caity segmentation the lungs

are segmented. Then it goes from feature e#traction part.there are three types of feature

e#traction that is EB5$?,: and ?0.finally it sends the sm classifier to classify the

image and compare to the database.then it prodce the result for either normal or

abnormal.

'.!,odu%es Description

'.!.1 Preprocessing

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n pre+processing step first applya:aussian filtering to our input image. :aussian

filtering is often used to remoe the noise from the image.:aussian filter is windowed

filter of linear class by its nature is weighted mean.The :aussian -moothing ,perator

performs a weighted aerage of surrounding pi#els based on the :aussian distribution. tis used to remoe :aussian noise and is a realistic model of defocused lens. -igma

defines the amount of blurring. The radius slider is used to control how large the template

is. Earge alues for sigma will only gie large blurring for larger template si%es. Hoise

can be added using the sliders.

'.!.! -.Contour Segmentation

Accuracy of these techniques is highly dependent on initial contour initiali%ation or

seed point locali%ation. 1ost of these methods assume the foreground ob/ect to hae a

uniform

structure which is diff erent from bac&ground pi#els. n case of caities$ only the border is

isible whereas the inside of caity shares similar characteristics with other lung tissues

due to " pro/ection. To address these drawbac&s$ we propose a dynamic programming

based solution for caity segmentation. :ien a cost image$ dynamic programming can

be used to !nd a minimum (or ma#imum) cost path between two pi#els. -ince caities

are mostly elliptical in shape$ optimal path calculation is done in polar space. The polar

image is constructed by e#tracting a circular region of interest (R,) of radius R around

the seed

point gien as input by user. -tart and end point for the path calculation is set to the same

location to ensure a closed contour when the ma#imum cost path is pro/ected bac& to the

original image space.

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'.!.# +BP -eature E3traction

Eocal binary patterns (EB5) are a type of feature used for classification in

computerision. The EB5 feature ector is created in the following manner'

• "iide the e#amined window into cells (e.g. G3#G3 pi#els for each cell).

• ;or each pi#el in a cell$ compare the pi#el to each of its < neighbors (on its left+

top$ left+middle$ left+bottom$ right+top$ etc.). ;ollow the pi#els along a circle$

i.e. cloc&wise or counter+cloc&wise.

• *here the center pi#els alue is greater than the neighbors alue$ write G.

,therwise$ write . This gies an


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?oweer$ the EB5 operator is not directly affected by the gray alue of P$ so we

can redefine the function as following'

T ≒ t (P+PG$ P+P$ S$ P+P


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'.!.4 S, C%assiier

(i).data setup' our dataset contains three classes$ each H samples. The data is "

plot original data for isual inspection

(ii).-61 with linear &ernel (+t ). *e want to find the best parameter alue C

using +fold cross alidation (meaning use GD data to train$ the other

GD to test).

(iii).After finding the best parameter alue for C$ we train the entire data

again using this parameter alue

(i). plot support ectors

(). plot decision area

-61 maps input ectors to a higher dimensional ector space where an optimal

hyper plane is constructed. Among the many hyper planes aailable$ there is only one

hyper plane that ma#imi%es the distance between itself and the nearest data ectors of

each category. This hyper plane which ma#imi%es the margin is called the optimal

separating hyper plane and the margin is defined as the sum of distances of the hyper

plane to the closest training ectors of each category.

CHAPTER )

RESU+T ND I,P+E,ENTATION

).1SCREEN SHOTS

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function varargout = Main(varargin)% MAIN M-file for Main.fig

% MAIN, by itself, creates a new MAIN or raises theexisting

% singleton.

%

% ! = MAIN returns the han"le to a new MAIN or thehan"le to

% the existing singleton.%

% MAIN(#$A&A$'#,hbect,event*ata,han"les,...) calls

the local

% function na+e" $A&A$' in MAIN.M with the giveninut argu+ents.

%

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% MAIN(#roerty#,#alue#,...) creates a new MAIN orraises the

% existing singleton. /tarting fro+ the left,roerty value airs are

% alie" to the 01I before Main2ening3cn gets

calle". An% unrecogni4e" roerty na+e or invali" value +a5esroerty alication

% sto. All inuts are asse" to Main2ening3cn via

varargin.

%% /ee 01I tions on 01I*6#s 7ools +enu. $hoose 801I

allows only one% instance to run (singleton)8.

%

% /ee also9 01I*6, 01I*A7A, 01I!AN*6/ % 6"it the above text to +o"ify the resonse to hel Main

% ast Mo"ifie" by 01I*6 v:.; ul-:


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function Main2ening3cn(hbect, event"ata, han"les,varargin)

% 7his function has no outut args, see utut3cn.% hbect han"le to figure

% event"ata reserve" - to be "efine" in a future version of

MA7A&% han"les structure with han"les an" user "ata (see01I*A7A)

% varargin co++an" line argu+ents to Main (see AHAH0IN)

% $hoose "efault co++an" line outut for Mainhan"les.outut = hbect

% 1"ate han"les structure

gui"ata(hbect, han"les)

% 1IAI7 +a5es Main wait for user resonse (see 1IH6/1M6)% uiwait(han"les.figure?)

% --- ututs fro+ this function are returne" to the co++an"line.

function varargout = Main2utut3cn(hbect, event"ata,han"les)

% varargout cell array for returning outut args (see

AHAH017)

% hbect han"le to figure% event"ata reserve" - to be "efine" in a future version of

MA7A&

% han"les structure with han"les an" user "ata (see

01I*A7A)

% 0et "efault co++an" line outut fro+ han"les structurevarargoutF?G = han"les.outut

% --- 6xecutes on button ress in ushbutton?.function ushbutton?2$allbac5(hbect, event"ata, han"les)

% hbect han"le to ushbutton? (see 0$&)


MA7A&% han"les structure with han"les an" user "ata (see

01I*A7A)

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set(han"les.text:,#/tring#,#Inut I+age Is Hea"ing....#)

global na+e athna+e i+ageCfilena+e athna+eD=uigetfile(#.g#,#/elect An I+age#)

Cathstr, na+e, extD = filearts(filena+e)

i+age=i+rea"(Cathna+e filena+eD) axes(han"les.axes?)i+show(i+age)

title(#Inut I+age#,#fontsi4e#,??,#fontna+e#,#$a+bria#)

axis eJualaxis off

% --- 6xecutes on button ress in ushbutton:.function ushbutton:2$allbac5(hbect, event"ata, han"les)

% hbect han"le to ushbutton: (see 0$&)

% event"ata reserve" - to be "efine" in a future version ofMA7A&% han"les structure with han"les an" user "ata (see

01I*A7A)

set(han"les.text:,#/tring#,#Noise He"uction in lungI+age....#)

global i+age rei+ageC+ n cD=si4e(i+age)

if c==K

i+age=rgb:gray(i+age)

else i+age=i+age

en"

rei+age=wiener:(i+age,CK KD) %filtering i+age using

wiener filtersaxes(han"les.axes:)

i+show(rei+age)title(#3iltere" I+age#,#fontsi4e#,??,#fontna+e#,#$a+bria#)

% --- 6xecutes on button ress in ushbuttonK.function ushbuttonK2$allbac5(hbect, event"ata, han"les)

% hbect han"le to ushbuttonK (see 0$&)



01I*A7A)

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set(han"les.text:,#/tring#,#In I+age /eg+entation

rocess....#)global rei+age

global binaryI+ageK

vesi+age=CDrei+age?=CDfont/i4e = :


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5eeer&lobsI+age = is+e+ber(labele"I+age, 5eeerIn"exes)binaryI+ageK = i+fill(5eeer&lobsI+age


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% alha = .


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seg = region2seg(i+age, +, :


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% --- 6xecutes on button ress in ushbuttonR.

function ushbuttonR2$allbac5(hbect, event"ata, han"les)% hbect han"le to ushbuttonR (see 0$&)


MA7A&% han"les structure with han"les an" user "ata (see01I*A7A)

% --- 6xecutes on button ress in ushbutton?


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% han"les structure with han"les an" user "ata (see01I*A7A)

% --- 6xecutes on button ress in ushbutton?L.

function ushbutton?L2$allbac5(hbect, event"ata, han"les)% hbect han"le to ushbutton?L (see 0$&)% event"ata reserve" - to be "efine" in a future version of

MA7A&


01I*A7A)

% --- 6xecutes on button ress in ushbutton?@.

function ushbutton?@2$allbac5(hbect, event"ata, han"les)

% hbect han"le to ushbutton?@ (see 0$&)% event"ata reserve" - to be "efine" in a future version ofMA7A&


01I*A7A)

% --- 6xecutes on button ress in ushbutton?R.function ushbutton?R2$allbac5(hbect, event"ata, han"les)

% hbect han"le to ushbutton?R (see 0$&)



01I*A7A)

% --- 6xecutes on button ress in ushbutton:


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% event"ata reserve" - to be "efine" in a future version ofMA7A&


% --- 6xecutes on button ress in ushbutton::.function ushbutton::2$allbac5(hbect, event"ata, han"les)

% hbect han"le to ushbutton:: (see 0$&)



01I*A7A)function varargout = 3eature2+ain?(varargin)

% 36A71H62MAIN? M-file for 3eature2+ain?.fig

% 36A71H62MAIN?, by itself, creates a new 36A71H62MAIN?or raises the existing% singleton.

%

% ! = 36A71H62MAIN? returns the han"le to a new

36A71H62MAIN? or the han"le to% the existing singleton.

%%

36A71H62MAIN?(#$A&A$'#,hbect,event*ata,han"les,...)

calls the local

% function na+e" $A&A$' in 36A71H62MAIN?.M with thegiven inut argu+ents.

%

% 36A71H62MAIN?(#roerty#,#alue#,...) creates a new

36A71H62MAIN? or raises the% existing singleton. /tarting fro+ the left,

roerty value airs are% alie" to the 01I before 3eature2+ain?2ening3cn

gets calle". An

% unrecogni4e" roerty na+e or invali" value +a5es

roerty alication% sto. All inuts are asse" to

3eature2+ain?2ening3cn via varargin.

%

% /ee 01I tions on 01I*6#s 7ools +enu. $hoose 801Iallows only one

% instance to run (singleton)8.

46


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%% /ee also9 01I*6, 01I*A7A, 01I!AN*6/

% 6"it the above text to +o"ify the resonse to hel

3eature2+ain?

% ast Mo"ifie" by 01I*6 v:.; :;-3eb-:


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% $hoose "efault co++an" line outut for 3eature2+ain?han"les.outut = hbect

% 1"ate han"les structure

gui"ata(hbect, han"les)

% 1IAI7 +a5es 3eature2+ain? wait for user resonse (see1IH6/1M6)

% uiwait(han"les.figure?)

% --- ututs fro+ this function are returne" to the co++an"

line.function varargout = 3eature2+ain?2utut3cn(hbect,

event"ata, han"les)

% varargout cell array for returning outut args (seeAHAH017)% hbect han"le to figure


MA7A&


% 0et "efault co++an" line outut fro+ han"les structure

varargoutF?G = han"les.outut

% --- 6xecutes on button ress in ushbutton?.

function ushbutton?2$allbac5(hbect, event"ata, han"les)

% hbect han"le to ushbutton? (see 0$&)

% event"ata reserve" - to be "efine" in a future version ofMA7A&


set(han"les.text,#/tring#,#6xtracting & 3eatures....#)

global binaryI+ageK i+age lungglobal lbfea

C+ n cD=si4e(binaryI+ageK)

i+age=i+resi4e(i+age,C+ nD)

lung=4eros(+,n)lung(binaryI+ageK)=i+age(binaryI+ageK)

% feature=i+hist(lung)

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% figure,lot(feature)% set(han"les.uitable?,#"ata#,feature)

/=C-? -? -?


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set(han"les.uitableK,#visible#,#on#)set(han"les.textK,#visible#,#on#)

set(han"les.uitableK,#"ata#,features#)save features features

% --- 6xecutes on button ress in ushbutton.function ushbutton2$allbac5(hbect, event"ata, han"les)% hbect han"le to ushbutton (see 0$&)


MA7A&


set(han"les.text,#/tring#,#$lassifying ungs....#)

Hesult?

% loa" target% grous=target% figure(#Na+e#,#0rah for $lassification rocess#)

% yli+(C-? KD)

% hol" on

% lot(grous(?9Q),#g.#)% hol" on

% lot(C-Kones(?,Q) grous(L9?Q)D,#rS#)%

title(#$A//I3I$A7IN#,#fontsi4e#,:


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CHAPTER 4

54


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PROPOSED S2STE,

n 5roposed wor& going to implement multi R61 classifier with some other

e#tracting features. A system framewor& is presented to recogni%e multiple &inds of

actiities from a R61 multi+class classifier with a binary tree architecture. The thought

of hierarchical classification is introduced and multiple R61s are aggregated to

accomplish the recognition of actions. 0ach R61 in the multi+class classifier is trained

separately to achiee its best classification performance by choosing proper features

before they are aggregated. The main adantage of multiple classification is diide into

the normal stage$ moderate stage$ beginning stage or seere stage.

CHAPTER 5

55


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CONC+USION

*e hae proposed a noel technique to automatically segment caities based on

dynamic programming which uses the li&elihood map output of pi#el classi!er as cost

function. *e hae alidated our results with those obtained by three human e#pert

readers on a large dataset including prominent as well as subtle caities. ,ur results are

ery encouraging and comparable with the degree of oerlap between trained human

readers and a chest radiologist. Cases with low inter+obserer agreement often contain

subtle caities or caities in the diseased regions. This indicates that accurate caity

segmentation is a difficult problem. ,ur wor& has a few limitations. n some cases the

dynamic programming is attracted to rib borders. The accuracy of our technique for

difficult caities can be increased by improing the pi#el classi!er and optimi%ing the

parameters for dynamic programming. t may be possible to deelop pi#el based features

more speci!c to caity borders so as to diff erentiate it with ribs and other bone structures.

Alternatiely we could include a rib suppression technique.

The dynamic programming path can be calculated more precisely if a few reference

points on the contour are clic&ed and the path is forced to pass through those points.

5roiding more than one reference point can be useful for subtle caities for precise

boundary segmentation. -uch a tool could be ery helpful in treatment monitoring for

tuberculosis.

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RE-ERENCES

KGL Antani$-$Candemir$-$;olio$E$=aeger$-$Qarargyris$A$-iegelman$=$U

a$:$GI9Automatic screening for tuberculosis in chest radiographs A

surey$9Vuant. mag. 1ed. -urg.$ ol. I$ no. $ pp. O.

K3L Ter?aarRomeny$B Uan :inne&en$B 9Automatic segmentation oflung fields

in chest radiographs$9 1ed. 5hys.$ ol. @$ no. G$ pp. >>27>22.

K@L Rachna $?.B$ 1alli&ar/una-wamy GI9"etection of Tuberclosis Bacilliusing

mage 5rocessing Technique9nternational/ournalof soft computingand

engineering --H'IG+I@$ol.I.

K


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KGL:inne&en$B$ Ric& $?.?.1 $Eaurence ?ogweg $ 5ragnya 1ads&ar$ 8

Automated

scoring of chest radiographs for tuberculosis prealence$9 000 Trans.1ed. mag$

ol.G$no. 7OII.

KGGL ?aithenBouussaid$asonas Qo&&inos$ Hi&os 5aragios$ GG 8"iscriminatie

Eearning

,f "eformable Contour 1odels9$ 000 Trans1ed.mg.

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