RULE-BASED LUNG REGION SEGMENTATION AND NODULE DETECTION VIA GENETIC ALGORITHM TRAINED TEMPLATE MATCHING

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1 İsanbul Ticare Üniversiesi Fen Bilimleri Dergisi Yıl: 6 Sayı:11 Bahar 00/1 s RULE-BASED LUNG REGION SEGMENTATION AND NODULE DETECTION VIA GENETIC ALGORITHM TRAINED TEMPLATE MATCHING Serha ÖZEKES * ABSTRACT A compuer-aided deecion (CAD) sysem was developed for deecing lung nodule paerns, which generally appear as circular areas of high opaciy on serial-secion Compued omography (CT) images. Firs of all, rule-based segmenaion of lung region was performed. Then, our mehod deeced he regions of ineres (ROIs) using he densiy values of pixels in CT images and scanning he pixels in direcions by using various hresholds. Then o reduce he number of ROIs he amouns of change in heir locaions based on he upper and he lower slices were examined, and finally a nodule emplae based algorihm was employed o caegorize he ROIs according o heir morphologies. To calculae he parameers of he emplae, a geneic algorihm process was employed as an opimizaion mehod. To es he sysem s efficiency, we applied i o 6 normal and abnormal CT images of 1 paiens wih 13 nodules. The experimenal resuls showed ha he sysem achieved 3.% sensiiviy wih 0. false posiives per image. Keywords: Lung Region Segmenaion, Templae Maching, Geneic Algorihm, Lung Nodule Deecion, Compuer Aided Deecion AKCİĞER BÖLGESİNİN KURAL TABANLI BÖLÜTLENDİRİLMESİ VE GENETİK ALGORİTMA KULLANILARAK EĞİTİLMİŞ ŞABLON EŞLEME YÖNTEMİYLE NODÜL TESİPİTİ ÖZET Bu çalışmada akciğer bilgisayarlı omografi (BT) görünülerindeki nodüllerin bilgisayarlı espii gerçekleşirilmişir. Öncelikle akciğer bölgesinin kural abanlı bölülendirilmesi gerçekleşirilmişir. Ardından BT görünülerindeki yoğunluk değerleri ve eşik değerleri ile yönlü arama yapılarak ilgi alanları belirlenmişir. İlgi alanlarının sayısını azalmak amacıyla al ve üs kesilerdeki konum değişimleri incelenmişir. Son olarak şablon eşleme abanlı bir yönem ile ilgi alanları şekilsel özelliklerine göre sınıflandırılmışır. Şablonun değerlerinin hesaplanması için geneik algorima yönemi kullanılmışır. Çalışmanın es edilmesi amacıyla 13 ade nodül bulunan 1 hasaya ai 6 normal ve anormal görünü kullanılmışır. Sonuça duyarlılığın görünü başına 0. yanlış poziif oranıyla %3. a ulaşığı görülmüşür. Anahar Kelimeler: Akciğer Bölge Tespii, Şablon Eşleme, Geneik Algorima, Akciğer Nodül Tespii, Bilgisayar Desekli Tespi * Isanbul Commerce Universiy, Vocaional School, Kucukyalı, Isanbul

2 Serha ÖZEKES 1. INTRODUCTION The moraliy rae for lung cancer is higher han ha for oher kinds of cancers around he world (Greenlee e al., 000). Of all he ypes of cancer, lung cancer is he mos common cause of deah and accouns for abou % of all cancer deahs. A he same ime, i appears ha he rae has been seadily increasing. No smoking is considered he mos effecive way o reduce he incidence of lung cancer in mos counries, while deecion of suspicious lesions in he early sages of cancer can be considered he mos effecive way o improve survival. Serial secion CT has been shown o increase lung umor deecion raes by %, compared wih radiologiss resuls using only projecion ches X-rays, and he average size of umors deeced has been dramaically reduced from 30 mm o 1 mm. Whereas a horacic CT scan using a single deecor scanner ypically generaes 0 o 100 axial image slices, he newer, muli-deecor scanners ypically generae 300 o 600 image slices. To read and inerpre hese massive amouns of image daa requires significan radiologis effor and predisposes he screening process o human error and missed deecion of cancerous lesions. Thus, compuer-aided diagnosic (CAD) approaches are becoming increasingly necessary for boh reducing radiologiss effor and improving deecion sensiiviy. Various CAD mehods have been proposed o deec lung nodules. Giger e al. obained nodules using muliple gray-level hresholding and a rule-based approach (Giger e al., 1). Armao e al. inroduced some 3D feaures, and performed feaure analysis by a linear discriminan analysis (LDA) classifier (Armao e al., 1). Kanazawa e al. used fuzzy clusering and a rule-based mehod (Kanazawa e al., 1). Penedo e al. se up Neural neworks (NNs), wih he firs one deecing he suspeced areas, and he second one acing as a classifier (Penedo e al., 1). For example, Xu e al. describe a sysem which, following proper radiogram preprocessing, uilizes a se of decision rules and a feed forward neural nework o find nodular paerns (Xu e al., 1). Following a differen approach, Lo e al. propose a wo-sage sysem: he firs one locaes possible nodular paerns (hus performing a sor of aenion focusing process) while he second, implemened by a convoluional neural nework, discriminaes nodules from non-nodules (Lo e al., 1). A prior model was developed by Brown e al. o find nodules on he baseline scan and locaed nodules in he follow up scans (Brown e al., 001). In his sudy, we designed a CAD sysem for lung nodule deecion in CT images. Firs, rule-based segmenaion of he lung region was performed in order o decrease he number of ROIs and compuaion ime. Using he densiy values of pixels in image slices and scanning hese pixels in direcions wih disance hresholds, ROIs were found. In order o classify he ROIs, a locaion change hresholding was used followed by a emplae maching based algorihm. The parameers of he emplae were calculaed using GA. Hence, he rue lung nodules were deeced successfully. 1

3 İsanbul Ticare Üniversiesi Fen Bilimleri Dergisi Bahar 00/1. MATERIALS AND METHODS The primary challenge for radiologiss and CAD sysems alike for lung umor deecion is ha, in serial secional images, here are many objecs ha have he same appearance and pixel inensiy as umor nodules. In a serial-secion slice, a cylindrical vessel can appear circular, and many vessels in he lung have a similar diameer o he lesions of ineres. A primary failing poin of all he CAD sysems referenced above is ha hey depend upon a firs-pass deecion of candidaes based on D image feaures, producing hundreds of firs-pass candidaes. The CAD sysems hen employ various schemes o ackle he enormous ask of removing likely false posiives from he vas candidae pool, wih varying degrees of success. A common problem is ha in filering ou he large volume of false posiives, rue posiives are also omied; creaing a sysem ha is prone o missing rue umors ye mainains a relaively high false posiive coun. The approach described herein was moivaed by he observaion ha experienced radiologiss screen for lung lesions no by considering individual image slices independenly, bu by paging hrough he image sack looking for 3D appearance characerisics ha disinguish umors from vessels. On consecuive images, vessels mainain a similar cross-secional size and heir in-plane circular appearance appears o drif across he viewing screen from one slice o he nex, following he oruous anaomy of he vessel. True lung nodules, in conras, appear seemingly ou of nowhere as circular objecs ha remain a approximaely he same on-screen locaion from slice o slice. Their size quickly increases and hen jus as rapidly decreases and he umor disappears afer a few slices. We developed a CAD scheme by compuer programs ha could prepare quaniaive values and he posiion of lesions o radiologiss. If radiologiss ake ino accoun he informaion obained from our CAD sysem, heir diagnosic performance would be higher. Figure 1 shows he overview of our CAD sysem. Figure 1. Procedural Flowchar for Deecing Lung Nodules 1

4 Serha ÖZEKES For he developmen and evaluaion of he proposed sysem we used he Lung Image Daabase Consorium (LIDC) daabase (Samuel e al, 00). In he LIDC daabase for each nodule, 6 exper lung radiologiss provided annoaions, i.e. segmenaions, of all nodules using 3 differen mehods, one manual and wo auomaic, for a oal of 1 possible radiologis/mehod combinaions. Each CT slice used in his sudy is formed of a 16 bi ASCII coded marix wih dimensions 1 X 1. These ASCII codes are relaed wih he densiy values of each pixel in slices. Using hese ASCII codes densiy values in Hounsfield unis (HU) are calculaed. HU is a uni of x-ray aenuaion used for CT scans, each pixel being assigned a value on a scale on which air is -1000, waer is 0, and compac bone is (Hounsfield, 10). When he daase was examined, i was deermined ha densiy values of he nodules were beween -00 HU and 100 HU, called as minimum densiy hreshold and maximum densiy hreshold values respecively.. 1. Rule-Based Lung Region Segmenaion Here are he seps of lung region segmenaion: Sep 1: Thresholding. As seen in Figure a shapes like nodules, bones and vessels are brigher han oher srucures. This means hey have higher HU values. Thus hresholding was performed in order o exrac he lung region roughly. If I(x, y) is he inpu image seen in Figure a, by applying he following rule Figure b was achieved. Here 1 represens whie and 0 represens black. IF I(x, y) < -00 HU THEN I(x, y) = 1 ELSE I(x, y) = 0 Sep : Labeling and small black shape eliminaion. As seen in Figure b he whie lung region conains small black shapes represening nodules and vessels. To eliminae hem we label he black shapes using conneced componen labeling (CCL) and analyze heir sizes. When all black shapes in he lung region seen in Figure b were labeled, heir sizes were analyzed using he following rule. If S(k) represening he size of kh black shape in pixels is smaller han 10 pixels, we assign 1 o he I(x, y) which is represening he pixels of kh shape of he image. Thus by eliminaing he small black shapes in Figure b, Figure c was achieved. IF S(k)<10 pixels THEN I(x, y)=1 0

5 İsanbul Ticare Üniversiesi Fen Bilimleri Dergisi Bahar 00/1 Sep 3: Labeling and lung region exracion. To exrac he lung region in Figure c, all whie shapes were labeled using CCL and heir morphologies were analyzed. As seen in Figure c he aspec raio lung region is smaller han he oher shapes. The morphologies of he shapes were analyzed using he following rule where AR(k) represens he aspec raio of kh shape which is he raio of heigh o widh, W(k) represens he widh of kh shape and I(x, y) represens he pixels of kh shape of he image. According o his rule he shapes, whose aspec raios were less hen 1. or widh were less hen 0 pixels, were eliminaed and hus he lung region was exraced. IF AR(k)>1. OR W(k)<0 pixels THEN I(x, y)=0 A he end of he hird sep he lung region of Figure a was segmened as Figure d and Figure e... Regions of Ineres Specificaion Mehods Pixels, which form he candidae lung nodule region, mus be members of a se of adjacen neighbor pixels wih densiies beween minimum densiy hreshold and maximum densiy hreshold values. I has been observed ha diameers of lung nodules are beween upper and lower boundaries. So, o undersand wheher a pixel is in he cener region of he shape, firs, diameer of he shape (assuming he pixel in quesion is he cener) should be considered. In his sage, we inroduce wo hresholds which form he boundaries. As inroduced in (Ozekes e al, 00), one is he minimum disance hreshold represening he lower boundary and he oher is he maximum disance hreshold represening he upper boundary. If a pixel has adjacen neighbors ha are less han minimum disance hreshold or more han maximum disance hreshold in direcions, i could be concluded ha his pixel couldn be a par of candidae lung nodule. Oherwise, i could be a par of candidae lung nodule. The values of minimum and maximum disance hresholds are deal wih he resoluion of he CT image. These hresholds are used o avoid very big or very small srucures such as pars of ches bones or hear and verical vessels. Thus he black and whie image of ROIs was obained showing ROIs in black..3. Firs Classificaion Using he Locaion Change Measuremen On serial images, vessels mainain a similar cross-secional size and heir in-plane circular appearance changes is locaion. Bu he rue lung nodules, remain a approximaely he same on-screen locaion from slice o slice. 1

6 Serha ÖZEKES (a) (b) (c) (d) (e) Figure. Seps of Rule-Based Lung Region Segmenaion, a) The Original CT Image, b) Thresholding, c) Labeling and Eliminaion of Small Black Shapes wihin The Lung Region, d) Labeling and Lung Region Exracion, e) Lung Region of The Original CT Image

7 İsanbul Ticare Üniversiesi Fen Bilimleri Dergisi Bahar 00/1 In his sep, o classify he ROIs he proposed CAD sysem measures he amoun of locaion changes of ROIs in serial secions. To specify he posiions, ROIs of he serial secions are labeled using conneced componen labeling. When a ROI in a CT slice is analyzed, i s all Euclidian disances o he ROIs in he upper and he lower slices are calculaed. The minimums of hese disances (Ed minupper and Ed minlower ) are compared wih he locaion change hreshold and each of he candidaes is classified according o he following decision rule inroduced in (Ozekes ve Camurcu, 006). IF Ed minupper > T lc OR Ed minlower > T lc THEN normal ELSE nodule candidae where Ed minupper and Ed minlower are he amouns of locaion change of he ROI based on he upper slice and he lower slice respecively. T lc is a hreshold value. If he ROI was classified as a normal srucure hen i was removed. Thus he new image of ROIs was obained wih reduced number of ROIs... Second Classificaion Using he Geneic Algorihm Trained Templae Maching Mehod To disinguish rue lung nodules from normal srucures by using heir morphologies we used lung nodule emplaes. Each pixel of CT images was scanned wih nodule emplaes and was looked for wheher here was a shape similar o he nodule in he emplae, so oo small, oo hin and oo long shapes were removed. If a similar one was deeced hen appropriae pixels of he shape are recorded as a par of a rue lung nodule. The GA process was employed as an opimizaion mehod o calculae he parameers of he emplae. The geneic algorihm is a mehod for solving opimizaion problems ha is based on naural selecion and is inspired by Darwin's heory abou evoluion. Algorihm is sared wih a random iniial se of soluions (represened by chromosomes) called populaion. Individual soluions from he curren populaion are aken o be parens and used o form a new populaion for he nex generaion. This is moivaed by a hope, ha he new populaion will be beer han he old one. Soluions which are seleced o form new soluions (offspring) are seleced according o heir finess - he more suiable hey are he more chances hey have o reproduce. This is repeaed unil he number of populaions is saisfied. Over successive generaions, he populaion evolves oward an opimal soluion. The general ouline of he geneic approach used in his paper can be summarized as follows: 3

8 Serha ÖZEKES 1. Encoding of he problem in a binary sring.. Random generaion of a populaion. 3. Exracion of he nodule emplae.. Reckoning of a finess value for each subjec.. Selecion of he subjecs ha will mae according o heir share in he populaion global finess. 6. Genomes crossover and muaions.. And hen sar again from poin.. If he sopping crierion has been saisfied, sop and decode he individual wih he highes finess o obain he emplae. A. Populaion Represenaion and Iniializaion Individuals are encoded as srings called chromosomes and composed of 0's and 1's. To calculae he parameers of a nodule emplae he number of variables has o be deermined and each variable has o be encoded wih appropriae bis. Having decided on he represenaion, he firs sep in he GA was o creae an iniial populaion randomly. B. Exracion of he Nodule Templae Chromosomes represen he binary codes of he elemens of he nodule emplae T. In his sep, each chromosome was decoded and he elemens of he emplae were compued in he inerval [-1, 1]. The emplae T wih dimensions x pixels is given below; T (1) As seen in (1), he emplae is symmerical, hus he oal number of variables was 10 ha are represened wih S and each elemen of S is being coded in binary; S () C. The Objecive and Finess Funcions The objecive funcion is used o provide a measure of how individuals have performed in he problem domain. In his sep, he image ha was seleced as he

9 İsanbul Ticare Üniversiesi Fen Bilimleri Dergisi Bahar 00/1 raining image was convolved wih he emplae belonging o he firs chromosome. This convoluion aims o classify he nodule from he oher srucures in he inpu image. If T(x, y) is he emplae wih dimensions n x m and I(x, y) is he inpu image wih dimensions M x N hen he convoluion of T wih I is wrien as, n 1 m1 C ( x, y) T I( x, y) T ( i, j) I( x i, y i) (3) i0 j0 Here * represens he convoluion operaion. The shapes, which are similar o he emplae, become sronger a he end of he convoluion compuaion. Therefore, he pixel values of hese shapes become high posiive, while he pixel values of nonsimilar ones become high negaive. Thus, o exrac he nodules from he convolved image C, we use he following decision rule: IF C(x,y) > 1 THEN C(x,y) =1 ELSE C(x,y) =-1 Afer he convoluion and he decision rule, he objecive funcion was compued beween his oupu image C and he desired arge image A. This process was repeaed wih he emplae ses belonging o each chromosome in he populaion. The objecive funcion has been seleced in his sudy as follows: M 1N 1 obj T C i, j Ai, j i0 j0 () where he symbol sands for he XOR operaion beween respecive pixels of C and A. Afer finding he objecive funcion, he associaed finess funcion was evaluaed for each chromosome according o he rule: finess( T ) M N obj( T ) () In his sudy for he sopping crierion he following rule was defined: sopcr 0. M N (6) If he maximum finess value of he chromosome was greaer han sopping crierion, he algorihm was sopped and he chromosome whose finess value was he maximum in he populaion was seleced. The emplae which has been exraced from his seleced chromosome was he mos proper emplae, which saisfied he ask desired o be realized.

10 Serha ÖZEKES D. Crossover and Muaion The geneic algorihm works by randomly selecing pairs of individual chromosomes o reproduce for he nex generaion. The probabiliy of a chromosome being seleced is proporional o is finess funcion value relaive o he oher chromosomes in he same generaion. To reproduce, a crossover procedure is defined. For crossover, in his sudy, an ineger posiion, i, was seleced uniformly a random beween 1 and he sring lengh, l, minus one [1, l-1], for he crossing sie. Then he wo chromosome srings were sliced a he sie, and he wo ail pieces are swapped and rejoined wih he head pieces o produce wo progenies. This crossover operaion was no necessarily performed on all srings in he populaion. Insead, i was applied wih a probabiliy Px when he pairs were chosen for breeding. A furher geneic operaor, called muaion, was hen applied o he new chromosomes, again wih a se probabiliy Pm. Muaion caused he individual geneic represenaion o be changed according o some probabilisic rule. In he binary sring represenaion, muaion caused a single bi o change is sae, 0 o 1 or 1 o 0. Afer recombinaion and muaion, he individual srings were hen, if necessary, decoded, he objecive funcion evaluaed, a finess value assigned o each individual and individuals seleced for maing according o heir finess, and so he process coninued hrough subsequen generaions. In his way, he average performance of individuals in a populaion was expeced o increase, as good individuals were preserved and bred wih one anoher and he less fi individuals die ou. The geneic algorihm was erminaed when he sopping crierion has been saisfied. 3. RESULTS For he evaluaion of he proposed sysem we used he LIDC daabase (Samuel e al, 00). To es he sysem s efficiency, we applied i o 13 normal and 13 abnormal slice images of 1 clinical cases wih 13 nodules. The diameers of he nodules were beween 3. and.3 millimeers and he hicknesses were beween.6 and 1. millimeers. ROI images were obained using direcion searches wih minimum disance of 1 pixel and maximum disance of pixels. A firs, 366 ROIs were specified by he ROI specificaion mehods. By he firs classificaion using he locaion change measuremen he number of ROIs was reduced o 6. And finally he second classificaion was performed using a emplae whose diameer was pixels. To calculae he parameers of he emplae he GA process was performed. A he end of he raining processes, geneic algorihm parameers were obained as in Table 1 and he emplae was found as in (). 6

11 İsanbul Ticare Üniversiesi Fen Bilimleri Dergisi Bahar 00/1 Table 1. Geneic Algorihm Training Parameers for Nodule Templae Opimizaion Parameers Values Number of Chromosomes per Populaion 100 Bis per Variable Number of Variables 10 Chromosome Lengh 0 Toal Bis in he Populaion 000 Crossover Probabiliy(Px) for breeding 0% Muaion Probabiliy 1% Generaion Gap % Templae Parameers Range [-1, 1] T () Afer convolving he image of ROIs wih he emplae T 13 ROIs were classified as nodules wih 16 FPs. An example of he original CT image, he segmened lung region, he exraced ROIs and he ROI classified as nodule are shown in Figure 3a, 3b, 3c, 3d and 3e respecively. The experimenal resuls showed ha he sysem achieved 3.% sensiiviy wih 0. FPs per image.. CONCLUSION A new scheme has been proposed o auomaically deec lung nodules in CT images. Rule-based lung segmenaion was performed and he morphological analysis of nodules was faciliaed using emplae maching echnique. The emplaes used in hese echniques were rained wih geneic algorihms. The resuls showed ha he proposed CAD sysem was an effecive assisan for human expers o deec nodule paerns and provide a valuable second opinion o he human observer.

12 Serha ÖZEKES (a) (b) (c) (d) (e) Figure 3. (a) The Original CT Image, (b) Rule-Based Segmened Lung Region, (c) Lung Region of The Original CT Image, (d) ROIs in The Lung Region, (e) Deeced Lung Nodule

13 İsanbul Ticare Üniversiesi Fen Bilimleri Dergisi Bahar 00/1. REFERENCES Armao, S. G., Giger, M. L., Moran, C. J., Blackburn, J. T., Doi, K., and MacMahon, H., (1), Compuerized Deecion of Pulmonary Nodules on CT Scans, Radiographics, 1, Brown, M. S., McNi-Gray, M. F., Goldin, J. G., Suh, R. D., Sayre, J. W., and Aberle, D. R., (001), Paien-Specificaion Models for Lung Nodule Deecion and Surveillance in CT Images, IEEE Transacions on Medical Imaging, 0, Giger, M. L., Bae, K. T., and MacMahon, H., (1), Compuerized Deecion of Pulmonary Nodules in Compued Tomography Images, Invesigae. Radiol.,, -6. Greenlee, R. T., Nurray, T., Bolden, S., and Wingo, P. A., (000), Cancer Saisics 000, CA Cancer J. Clin., 0, -33. Hounsfield, G. N., (10), Compued Medical Imaging, Med. Phys.,, 3-0. Kanazawa, K., Kawaa, Y., Niki, N., Saoh, H., Ohmasu, H., Kakinuma, R., e al., (1), Compuer-Aided Diagnosic Sysem for Pulmonary Nodules Based on Helical CT Images, In: K. Doi, H. MacMahon, ML. Giger, K. Hoffmann, eds., Compuer-Aided Diagnosis in Medical Imaging, Amserdam, The Neherlands: Elsevier Science, Lo, S. C. B., Lou, S. L. A., Lin, J. S., Freedman, M., Chien, M. V., and Mun, S. K., (1), Arificial Convoluional Neural Nework Techniques and Applicaions for Lung Nodule Deecion, IEEE Trans. Med. Imag., 1, Ozekes, S., Osman, O., and Camurcu, A. Y., (00), Mammographic Mass Deecion Using a Mass Templae, Korean J. Radiol, 6, 3, 1-. Ozekes, S., and Camurcu, A. Y., (006), Auomaic Lung Nodule Deecion Using Templae Maching, Lecure Noes in Compuer Science, 3, -3. Penedo, M. G., Carreira, M. J., Mosquera, A., and Cabello, D., (1), Compuer- Aided Diagnosis: A Neural-Nework-Based Approach o Lung Nodule Deecion, IEEE Transacions on Medical Imaging, 1, -0. Samuel, G., Armao, III. Geoffrey M., Michael, F., Charles, R., David, Y., e al., (00), Lung Image Daabase Consorium-Developing a Resource for The Medical Imaging Research Communiy, Radiology, 3-.

14 Serha ÖZEKES Xu, X. W., Doi, K., Kobayashi, T., MacMahon, H., and Giger, M., (1), Developmen of an Improved CAD Scheme for Auomaed Deecion of Lung Nodules in Digial Ches Images, Med. Phys.,,