Comparative Analysis of Feature Extraction Methods for Optic Disc Detection

Transcription

1 IOSR Journal of Computer Engneerng (IOSR-JCE) e-issn: , p- ISSN: Volume 16, Issue 3, Ver. III (May-Jun. 2014), PP Comparatve Analyss of Feature Extracton Methods for Optc Dsc Detecton Harsha Pancharya 1, Sangta Bharkad 2 1,2 Department of Electroncs Engneerng, Government Engneerng College, Aurangabad, Inda Abstract: Medcal mage analyss and processng has great sgnfcance n the feld of medcne, especally n non-nvasve treatment and clncal study. Normally fundus mages are manually graded by specally traned clncans n a tme-consumng and resource-ntensve process. A computer asssted fundus mage analyss could provde an mmedate detecton and characterzaton of retnal features pror to ophthalmologst s nspecton. Optc Dsc (OD) or Optc Nerve Head (ONH) s relevant anatomcal eye structure to dagnose varous dseases such as Glaucoma, Cardovascular dseases, Dabetc retnopathy n ts advanced stages. Extracton of OD n retnal fundus mage s also helpful n mage regstraton and serves as landmark for other retnal feature localzaton lke macula. Ths paper gves the bref survey of current OD detecton methods wth lmtatons and challenges. Keywords: Dabetc Retnopathy, fundus mages, Glaucoma, non-nvasve, Optc dsc. I. Introducton Optc Dsc connects eye and bran acts as a condut, transmttng vsual nformaton from lght senstve cells of retna to bran. It s also the entry pont of major blood vessel that supples blood to retna [1]. Glaucoma and Dabetc retnopathy are sght threatenng dseases whch n severe stage causes blndness. So, optc dsc detecton s an mportant step for dagnoss of these two dseases as well as some other dseases related to retna. Changes n optc dsc shape and area may ndcate dsease progresson. In case of glaucoma detecton, the optc cup-to-dsc rato (CDR) determnes whether the person s sufferng from glaucoma or not. Moreover, n case of Dabetc retnopathy n ts advanced stage can be determned by the neovascularzaton.e., detecton of the new fragle blood vessel n the ONH area. In fundus mages optc dsc appears as a brght yellowsh or whte regon. It s an entrance of blood vessels and optc nerve to the retna and often serves as a reference for other anatomcal features lke macula, temporal arcade of man blood vessel. Its shape s approxmately crcular, nterrupted by outgong vessels. OD s the convergence of blood vessel network and one of the most promnent objects n human retna. It s a vertcal oval, wth average dmensons of 1.76mm horzontally by 1.92mm vertcally [2]. The usual sze of OD s pxels but may vary from person to person.the normal as well as abnormal fundus mage consstng of OD s shown n Fgure 1. ONH s vertcally oval wth a perpheral rm composed of neural tssue and blood vessels. It has central depresson called as Optc cup. The outermost lmt of OD s known as dsc margn whch can be dentfed by ophthalmologst as whtsh or brght yellowsh at the nnermost perphery of neuro-retnal rm. (a) (b) Fg.1. Retnal mages (a) normal and (b) abnormal fundus mage, wth brght ovod shape as an OD The detecton of the OD n the human retna s a very mportant task. Because, the optc dsc has smlar attrbutes n terms of brghtness, color and contrast same as n case of exudate detecton n dabetc retnopathy. The optc dsc can be seen as a landmark and t can be used for a coarse regstraton of retnal mages n order to reduce the search space for better mage. Furthermore, ts detecton s a frst step n understandng ocular fundus mages; whether the mage s of left eye or rght eye. The dameter delvers a calbraton of the measurements that determnes approxmately the localzaton of the macula, the center of vson, whch s of great mportance as lesons n the macular regon affect vson mmedately. Moreover, OD segmentaton s key pont n fndng the varous dseases of the retna [3]: 1. Glaucoma detecton and dabetc retnopathy detecton by changes n CDR and neovascularzaton. 49 Page

2 2. Patents wth headache, sometmes have swollen optc ndcates rased ntracranal pressure (ICP) whch could be due to bran tumor, so presence of bran tumor can be roughly estmated by consderng the optc dsc shape. 3. Other dseases lke cardovascular dseases, optc neuropathes, optc neurts and paplledema can be dagnosed wth the help of changes n OD appearance. II. Feature Extracton Methods For OD Detecton Even though the OD has well-defned features and a characterstc, localzng the OD automatcally and n a robust manner s not as smple snce, the appearance of the OD may vary sgnfcantly due to retnal pathologes. In order to effectvely detect the OD, the varous methods developed should consder the varaton n appearance, sze, and locaton among dfferent mages [4]. The appearance of the yellowsh OD regon was characterzed by a relatvely rapd varaton n ntensty because of the dark blood vessels besde the brght nerve fbers. So, durng segmentaton n many cases OD cannot be segmented as whole crcular regon. In most of the paper researchers consdered the OD as the brghtest regon wthn retnal mage. However, ths crteron may not be applcable for retnal mages those nclude other brght regons because of dseases such as exudates due to dabetc retnopathy. Some consdered the OD as the area wth hghest varaton n ntensty of adjacent pxels. Both the crtera consdered by many researchers used ther algorthm for normal, healthy retnal mages. OD detecton can be classfed nto varous template-based, property-based, transform-based and vessel model-based methods as shown n Fg. 2 Fg.2. OD detecton methods 1.1. Template-based Method Youssf et al. [5] appled template matchng to locate the OD center. They frst used llumnaton equalzaton based and adaptve hstogram equalzaton to smooth the mage wth a runnng wndow. Adaptve hstogram equalzaton and normalzaton s done to enhance the contrast of an mage. Blood vessels are extracted by Matched flter on one scale and 12 drectons. Matched Flters are appled to roughly match the drecton of vessel n OD. The pxel wth least accumulaton s consdered as OD. Abramoff and Nemejer mentoned n the paper [6] that the approach has the potental to detect the locaton of the OD n retnal mages wth few or no abnormaltes and the poston n the mage whch s located n the mddle of the optc dsc. To accomplsh ths they used k-nn regresson to fnd the relatonshp between the dependent varable d, representng the dstance to the optc dsc center, and a feature vector measured around a crcular template. The template s crcular because the optc dsc s approxmately crcular tself. The crcular template s placed at varous locatons n the mage and measures the features around the template. Based on the feature vector the dstance crcular template s used and ndependent varables v s measured at the border and under the template. The tranng set s created storng dstance d to true object center. The estmated OD dstance d, d can be calculated by: 1 k d k 1 d. (1) Where d s the stored value of d of the th nearest neghbor of v and d locaton of object of nterest that s get detected. The optc dsc was consdered found when the system ndcated a poston n the mage nsde the manually segmented optc dsc the average dstance between the automatcally ndcated optc dsc center and 50 Page

3 the reference standard was approxmately 29 pxels. Lalonde et al. [7] localzed OD n low resoluton fundus mage usng Hausdorff based template matchng and pyramdal decomposton for trackng OD Transform-based Method Prncpal component analyss (PCA) has been used by Chutatape and L [8] to dfferentate the optc dsc from other man features n retnal mages. Brghtness guded, PCA model based method was proposed to detect the optc dsc as follows: T AA Av Av. (2) where Av s the Egen vector the subspace defned by the optc dsc s a lnear combnaton of the orgnal tranng-mage vectors and arranged n descendng order accordng to ts assocated egenvalue. A fundus coordnate system was establshed to provde a better descrpton of the features n the retnal mages. However, the tme consumed for PCA algorthm s more. They used PCA along wth actve shape model [9] to detect the exact boundary of optc dsc whch gves better results than ther prevous work. Haar [4] appled llumnaton equalzaton to the green-band of the RGB fundus mage, and then a resoluton pyramd usng a smple Haar-based dscrete wavelet transform was created. Fnally, the brghtest pxel at the ffth level of the resoluton pyramd was chosen to correspond to the OD area. Haar proposed an alternatve to the above method based on the pyramdal decomposton of both the vasculature and the greenband, where the ffth level of the resoluton pyramd for both the llumnaton equalzed green-band and the bnary vessel segmentaton are summed, and the hghest value corresponds to the OD center. In [10], Ghafar et al. appled the crcular Hough transform (CHT) to detect the OD whch has a roughly crcular shape. The retnal vasculature n the green-band mage was suppressed usng the closng morphologcal operator. The Sobel operator and a smple threshold were then used to extract the edges n the mage. CHT was fnally appled to the edge ponts, and the largest crcle was found corresponds to the OD. 1.3.Supervsed Learnng Method Hoover and Goldbaum [11] proposed a method for optc nerve detecton based upon an algorthm called fuzzy convergence. Ths algorthm dentfed the optc nerve as the focal pont of the blood vessel network. Fuzzy convergence uses the endponts of the blood vessel segments to help fnd the soluton. For every partton p t uses the followng equaton for dscrmnant statstcs F as follows: F p 2 A B 2 2 A B p. (3) Where and are the mean and standard devaton of each set. The largest value of ndcates the best partton. The method used the convergence of the blood vessel network; ths method also takes lot of tme to execute on sngle mage. 1.4.Vessel model based Method Akta et al. [12] localzed OD by back-trackng the vessels to ther orgn. It s one of the good methods to localze OD but the system has to rely on the vascular tree segmentaton whch s tme consumng. Grson et al. [13] proposed a method that s based on a model of the geometrcal drectonal pattern of the retnal vascular system. Ths system mplctly holds the nformaton on the OD poston as the pont of vessel convergence. Usng the vessel center-lne ponts and the correspondng vessel drectons provded by any vessel dentfcaton procedure, the model parameters were dentfed by a means of smulated annealng. These estmated values provde the coordnates of the OD center. Rangayyan et al. [15] detected OD by phase-portrat analyss. Assumng OD s near the focal pont of convergence of retnal vessel. The vasculature network s then extracted by Gabor flter. The ONH s obtaned va phase portrat analyss and ntensty based condton Morphology-based Method Sopharak et al. [16] used Fuzzy C-means clusterng algorthm and then morphologcal based method to detect OD. Frst they converted the RGB fundus mage nto HSI color space mage. After preprocessng on I band mage of HSI color space mage, the morphologcal closng operaton s done usng the equaton (4): ( B1) OP1 ( f I ). (4) Where OP s the closng operaton result on I band mage 1 f and B1 s the structurng element of sze I 51 Page

4 B1 {6, 7,8,9,10} vared accordngly for each mage. Then the threshold s appled on closed mage wth threshold value of 1 {0.5,0.6,0.7,0.8,0.9}.The morphologcal reconstructon by dlaton as s performed on prevous threshold mage as follows: OP3 RfI ( OP2). (5) The reconstructed mage OP3 s threshold. If the threshold mage conssts of exudates then the compactness crtera s appled to reduce the false detecton between exudates and OD. In [17], morphologcal flterng technques and actve contours are used to fnd the boundary of the optc dsc. Thomas et al. [18] have been used mathematcal morphology for detecton of OD. It s dvded nto two steps. In frst step, the papllary regon s localzed by explotng the lumnosty of paplla and the vasculature network. Ths s done because; the gray level varaton s hgher than any other part of retnal structure. Along wth ths the shade correcton technque s used to remove the background varaton. In the second step the boundary s detected by watershed transform usng external and nternal marker Property-based Method Snthanayothn et al. [19] detected the OD by dentfyng the area wth the hghest average varaton among adjacent pxels usng a wndow sze equal to that of the OD. The mages were preprocessed usng an adaptve local contrast enhancement method whch was appled to the ntensty component. But ths algorthm works n average when retnal lesons are present. ( d, jd ) arg max( p(, j) ). (6) Where ( d, jd) the center of detected OD s, the maxmum varance of sub-mage ( p (, j) ). B. Zhang et al. [20] proposed a method. In whch, frst of all mult-scale Gaussan flterng s done on fundus mage. Scale producton, and double threshold s done to ntally extract the vessel s drectonal map of varous thcknesses. The map s then thnned before another threshold s appled to remove pxels wth low ntenstes. In the second step, a Vessels Drectonal Matched Flter (VDMF) of varous dmensons s appled to the canddates to be matched, and the pxel wth the smallest dfference desgnated the OD center. Flow dagram for mult-scale Gaussan flterng based OD detecton method s shown n Fgure 3. Mult-scale Gaussan flter s represented by equaton (7): 2 1 x f( x, y) exp m 2 L 2 s 2s x t. s, y, 2. (7) Where f ( x, y ) s the threshold mage, when green plane mage I ( xy, ) s passed through the 2D-Gaussan curve. S represents the scale of the flter and m s gven by: ts 2 1 x m exp dx /(2 ts ) 2 2 2s ts s. (8) s used to normalze the mean value of the flter to 0. So that the smooth background can be removed after flterng. L s the length of the neghborhood along the y-axs to smooth nose and t s a constant. Fg.3. Flow dagram for mult-scale Gaussan flterng based OD detecton method 52 Page

5 In [14], Chrástek et al. appled an averagng flter to the green band mage of RGB fundus mage, and located the OD roughly at the pont of the hghest average ntensty. Koozekanan et al. [21] detected OD by Mnmum ntensty Maxmum Soldty method. They assumed that OD should be typcally at the ntersecton of the reddest and one of the brghtest regon of fundus mage. Summaton of the ndvdual ntenstes wth brght pxel as 1 and dark red pxel as 0 s taken. Thus, OD wll be detected by ntersecton wth mnmum pxel ntensty and maxmum soldty, where soldty s measure of compactness. Table I shows comparatve success rate results of exstng methods wth dfferent databases. Table I Comparatve Success rate analyss for exstng methods of OD detecton Sr. no OD detecton Methods Local Database STARE Database DRIVE Database 1. Template matchng and matched flter [5] 100% 98.8% - 2. Crcular template matchng and k-nn regresson [6] 99.9% Hausdorff based template matchng [7] 100% 71.6% - 4. PCA based method [8] 99% Haar based DWT [4] 89% 70.4% - 6. Pyramdal decomposton [4] 93.2% 54.3% - 7. Fuzzy Convergence [11] 97.4% 65.4% Geometrc parametrc model [13] 97.53% Gabor Flter and phase portrat analyss [15] - 69% 100% 9. Morphology based [18] 100% 58% Varaton of Intensty [19] 99.1% 42% Gaussan flterng [20] 99.25% Mnmum ntensty maxmum soldty [21] 99.1% 98.68% 100% * (-) ndcates no nformaton s provded n relatve feld III. Concluson Varous methods for locatng the OD n retnal fundus mage are presented n ths paper. An overvew of these results shows that some of the exstng methods reach better overall performance; some of them provdes the shortest computatonal tme and gves better effcency. But these all methods are not robust as the OD segmentaton suffers from many problems lke erroneous detecton along wth exudates, nsuffcent results n nosy mages, n poor contrast and n uneven-llumnaton. The most challengng part of OD segmentaton s preprocessng as each fundus mage taken vary by contrast, color etc. The exstng algorthm does not work well for the entre standard databases whch are publcally avalable. Thus, OD segmentaton n medcal magng s the challengng job for the researchers preprocessng the orgnal mage before further processng on t s key step n detecton of OD. Better preprocessng gves the OD segmentaton results up to the mark. Hence there s a need of OD detecton algorthms whch gves the better performance on nosy databases. References [1] Danel D. Chras. Human Bology. Jones and Bartlett Learnng, 7 th edton, [2] H.Yng, M.Zhang and J.-C Lu., Fractal-based automatc localzaton and segmentaton of optc dsc n retnal mages, n Proc. of IEEE Conf. on Engneerng n Medcne and Bology Socety(EMBS).,2007 [3] [4] F. Haar, Automatc localzaton of the optc dsc n dgtal colour mages of the human retna, M.S. thess, Utrecht Unversty, Utrecht, The Netherlands,2005. [5] A.Youssf, A Ghalwsh, and A Ghonem, Optc dsc detecton from normalzed dgtl fundus mages by means of vessels drecton matched flter, n IEEE Trans. on Medcal Imagng, vol. 27, no. 1, pp , [6] M. D. Abramoff, Mendert Nemejer, The automatc detecton of the optc dsc locaton n retnal mages usng optc dsc locaton regresson, n Proc. of IEEE Conf. on Eng Med Bol Soc, pp , [7] M. Lalonde, M. Beauleu, and L. Gagnon, Fast and Robust Optc Dsc Detecton Usng Pyramdal Decomposton and Hausdorff- Based Template Matchng, n IEEE Trans.On Medcal Imagng,vol. 20, no. 11, Nov [8] H. L and O. Chutatape, Automatc locaton of optc dsc n retnal mages, n Proc. IEEE Conf. on Image Processng.,vol. 2,pp Oct. 7 10, [9] H.L and O.Chutatape, Automated Feature Extracton n Color Retnal Images by a Model Based Approach, n IEEE Transacton on Bomed. Engneerng, vol.51,no.2, Feb [10] R. A. Abdel-Ghafar, T. Morrs, T. Rtchngs, and I. Wood, Detecton and charactersaton of the optc dsk n glaucoma and dabetc retnopathy, n IEEE Conf. on Med. Image Understand Annual, London, U.K., Sep ,2004. [11] A. Hoover and M. Goldbaum, Locatng the optc nerve n a retnal mage usng the fuzzy convergence of the blood vessels, n IEEE Trans. on Med. Imagng., vol.22,no.8,pp ,Aug [12] K. Akta and H. Kuga, Pattern recognton of blood vessel networks n ocular fundus mages, n Proc.of IEEE conf., pp ,1982. [13] M.Foraccha, E.Grson, A.Rugger, Detecton of Optc dcs n retnal mages by means of geometrcal model of vessel structure, n IEEE Trans.on Medcal Imagng, vol.23. no10.pp ,2004. [14] R. Chrástek, M. Wolf, K. Donath, G. Mchelson, and H. Nemann, Optc dsc segmentaton n retnal mages, Bldverarbetung für de Medzn, Publsher-Sprnger Berln Hedelberg. pp , Page

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