Improvement of Automatic Hemorrhages Detection Methods using Brightness Correction on Fundus Images

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1 Improvement of Automatc Hemorrhages Detecton Methods usng Brghtness Correcton on Fundus Images Yuj Hatanaka *a, Toshak Nakagawa *b, Yoshnor Hayash *c, Masakatsu Kakogawa *c, Akra Sawada *d, Kazuhde Kawase *d, Takesh Hara *b, Hrosh Fujta *b a Dept. of Electronc Control Engneerng, Gfu Natonal College of Technology Kammakuwa, Motosu-sh Gfu , Japan b Dept. of Intellgent Image Informaton, Dvson of Regeneraton and Advanced Med. Scence Graduate School of Medcne, Gfu Unversty, 1-1 Yanagdo, Gfu-sh Gfu , Japan c Tak Co., Ltd., Kono, Ogak-sh Gfu , Japan d Dept. of Ophthalmology, Gfu Unversty, School of Medcne 1-1 Yanagdo, Gfu-sh Gfu , Japan ABSTRACT We have been developng several automated methods for detectng abnormaltes n fundus mages. The purpose of ths study s to mprove our automated hemorrhage detecton method to help dagnose dabetc retnopathy. We propose a new method for preprocessng and false postve elmnaton n the present study. The brghtness of the fundus mage was changed by the nonlnear curve wth brghtness values of the hue saturaton value (HSV) space. In order to emphasze brown regons, gamma correcton was performed on each red, green, and blue-bt mage. Subsequently, the hstograms of each red, blue, and blue-bt mage were extended. After that, the hemorrhage canddates were detected. The brown regons ndcated hemorrhages and blood vessels and ther canddates were detected usng densty analyss. We removed the large canddates such as blood vessels. Fnally, false postves were removed by usng a 45-feature analyss. To evaluate the new method for the detecton of hemorrhages, we examned 125 fundus mages, ncludng 35 mages wth hemorrhages and 90 normal mages. The senstvty and specfcty for the detecton of abnormal cases was were 80% and 88%, respectvely. These results ndcate that the new method may effectvely mprove the performance of our computer-aded dagnoss system for hemorrhages. Keywords: pre-processng, detecton, retnal fundus mage, dabetc retnopathy, hemorrhage 1. INTRODUCTION The detecton of hemorrhages s one of the mportant factors n the early dagnoss of dabetc retnopathy (DR). The exstence of hemorrhages s generally used to dagnose DR or hypertensve retnopathy by usng the classfcaton scheme of Schee. In spte of detectng mcroaneurysms, t s dffcult for ophthalmologsts to fnd them n noncontrast fundus mages. The contrast observed n a mcroaneurysm mage s very low; therefore, ophthalmologsts usually detect mcroaneurysms by usng fluorescen angograms. However, t s dffcult to use fluorescen as a contrast medum for dagnosng all the medcal examnees subjected to mass screenng. Therefore, the patents who show the possblty of havng DR were thoroughly examned at a hosptal n Japan. The number of patents wth adult dseases such as dabetes and hypertenson s on the rse n Japan. To prevent or detect these dseases n early stages, ophthalmologsts rely on the examnaton of fundus mages obtaned from patents aged over 40 years durng complete health examnatons or mass screenngs. DR s a complcaton assocated wth dabetes, and there s a hgh probablty that dabetc patents wll develop ths condton wthn 10 years from the onset of dabetes. Furthermore, DR s the leadng cause of blndness. In Japan, there are approxmately 7.4 mllon patents wth dabetes and approxmately 16.2 mllon patents who may have dabetes [1]. Approxmately three mllon are thought to suffer from DR. Ths dsease can be prevented from developng nto blndness f t s treated at an early stage. *hatanaka@gfu-nct.ac.jp; phone ; fax ; gfu-nct.ac.jp Medcal Imagng 2008: Computer-Aded Dagnoss, edted by Maryellen L. Gger, Nco Karssemejer Proc. of SPIE Vol. 6915, 69153E, (2008) /08/$18 do: / Proc. of SPIE Vol E-1

2 However, t has been recorded that approxmately 3,000 people have lost ther vson followng the onset of DR. Fundus photographs obtaned by the fundus camera are used to dagnose DR. Japanese ophthalmologsts usually examne the presence of hemorrhages, mcroaneurysms, and exudates n order to dagnose DR. Recently, many studes have been reported on the use of fundus mages n the detecton DR [2 9]. Nemejer et al. proposed a method for the detecton of mcroaneurysms n fluorescen angograms [2 4]. Ther method comprsed two steps. Frstly, mcroaneurysms were detected by usng a watershed transform. The false postves were then reduced by relable vessel detecton n the vcnty of each mcroaneurysms canddate. Serrano et al. proposed a method for detectng mcroaneurysms by the regon growng technque n order to analyze fluorescen angograms [5]. Usher et al. presented a method for detectng hemorrhages, mcroaneurysms, and exudates [6, 7]. The regon growng method used by them segmented the abnormal and retnal regons. Nagayosh et al. presented a method that ncluded two addtonal processes to ths method [8]. One of these was the normalzaton of the color pxel values and the other was the use of the color pxel values for blood vessels. Moreover, the prevous method had the drawback of a long operaton tme of approxmately 43 s per mage [8]. We also reported a method for detectng hemorrhages and exudates n noncontrast fundus mages [9]. Although the senstvty for hemorrhages was 85%, the specfcty was 21%. In our prevous study, we had two problems the absence of a technque to normalze fundus mages and the removal of false postves. In ths study, we am to develop methods for fundus mage normalzaton and false postve elmnaton method n the noncontrast mages. 2. METHODS 2.1 Overall Scheme The flowchart for our overall detecton scheme s shown n Fg. 1. It conssts of seven stages: (1) mage dgtzaton, (2) mage normalzaton, (3) extracton of optc nerve head, (4) detecton of hemorrhage canddates, (5) elmnaton of false postves n blood vessels, (6) elmnaton of funcular-shaped false postves, and (7) elmnaton of false postves by feature analyss. Further detals are descrbed below. 2.2 Image dgtzaton One hundred forty fve fundus mages were captured usng a fundus camera and a flatbed-type scanner. Eghty seven fundus mages were obtaned at a resoluton of an array of 1,600 1,600 pxels wth 24-bt color, and 58 fundus mages were obtaned at a resoluton of an array of 2,800 2,800 pxels wth 24-bt color. An example of a color fundus mage s shown n Fg. 2 (a). Subsequently, the scale of the matrx was frst reduced to the VGA sze (wdth 640 pxels) by obtanng detaled subsamples from the orgnal mage data to mprove processng effcency. 2.3 Image normalzaton Due to the flash, there s an atypcal change n the color of the fundus mages. We suggested a scheme of brghtness correcton usng hue saturaton value (HSV) space. Frst, the brghtness values of the HSV space were calculated. The brghtness correcton value Bc( s gven by the followng equaton: Bc(, 1 { (, ) 1} 2 = V j (1) where V( s the brghtness value of the HSV space. Vj (, ) = MAX, MAX: Rj (, ), Gj (, ), Bj (, ) (2) Next, the red value R(, green value G(, and blue value B( changed by Bc( are gven by the followng equaton: Proc. of SPIE Vol E-2

3 Image dgtzaton Fundus mage normalzaton Extracton of optc nerve head Detecton of hemorrhage canddates Elmnaton of blood vessel canddates Elmnaton of funcular-shaped false postves Elmnaton of false postves on redbt mage by rule-based method Elmnaton of false postves on green-bt mage by rule-based method Elmnaton of false postves on bluebt mage by rule-based method Elmnaton of false postves on redbt mage by dscemment machne Elmnaton of false postves on green-bt mage by dscemment machne Elmnaton of false postves on bluebt mage by dscemment machne Results output Fg. 1. Flowchart for detectng hemorrhages on fundus mages. Hj (, ) H (, = mod6 60 Hj (, ) f(, = H (, 60 p (, = V(, [1 S (, ] q (, = V( [1 f( js ) (, ] t (, = V (, {1 [1 f(, ] S (, } f H (, = 0 R(, = V(,, G(, = t(,, B(, = p(, f H (, = 1 R( = q(, G( = V(, B( = p( f H (, = 2 R(, = p(,, G(, = V(,, B(, = t(, f H (, = 3 R(, = p(,, G(, = q(,, B(, = V(, f H (, = 4 R(, = t(,, G(, = p(,, B(, = V(, f H (, = 5 R(, = V(,, G(, = p(,, B(, = q(, (3) Proc. of SPIE Vol E-3

4 where H( s the hue value and S( s the saturaton value. G( B( , MAX MIN B( R( , MAX MIN H = R( G( , MAX MIN MAX MIN S = MAX MAX MIN S = MAX MIN : R(, G(, B( f f f MAX = R( MAX = G( MAX = B( (4) However, equaton (2) cannot be used when S( s zero. The brghtness was equally corrected from the center to the skrt regon of the fundus mage (as shown n Fg.2 (b)). The color fundus mages corrected by Bc( were then processed by gamma correcton. The gamma value was expermentally set to 1.5 (as shown n Fg.2 (c)). Fnally, the hstograms of each red, blue, and blue-bt were extended (as shown n Fg.2 (d)). The fundus mages were standardzed and made unclear by usng these processes. (a) (b) (c) (d) Fg. 2. Color contrast enhancement. (a) Orgnal color fundus mage. (b) Brghtness of fundus mage was changed by the nonlnear curve wth brghtness values of HSV space. (c) Image after processng by gamma correcton. (d) Image was adjusted n the dynamc range. Proc. of SPIE Vol E-4

5 2.4 Extracton of optc nerve head The color mages were converted nto the grayscale mages by selectng only the green component. Thus achevng a clearer contrast was obtaned as compared to the orgnal mages, whch ncluded all the color components. After the optc nerve head was hghlghted wth brghtness greater than that used for hghlghtng other tssues, t was nvestgated by the p-tle method [10]. The shape of the detected regon was approxmated to a crcle. 2.5 Detecton of hemorrhage canddates The pxel values of hemorrhages n an mage are lower than those of other regons. Therefore, the haemorrhages were detected by performng fnte dfference calculatons along wth smoothng. Ths method was carred out n two steps: the rough and detaled detecton processes. Frstly, the fundus mages were smoothed by usng a mask of 3 3 pxels. Next, the dfference n the pxel values between two smoothed mages was calculated. Subsequently, the hemorrhage and blood vessel canddates were segmented by the thresholdng technque. Fg. 3 (b) shows an mage of the roughly detected vessels. As shown n ths mage, the end of thn vessels was not detected. Therefore, the end of the vessels was detected by usng a smlar method that uses two types of mages smoothed wth a mask of 9 9 pxels. Fg. 3 (c) shows an mage of the detaled parts of the vessels. By combnng both types of technques for the detecton of hemorrhages and vessels, we could detect all the blood vessels from the optc nerve head up to the end of the vessels. Fg. 3 (d) shows an mage of the end of the vessels. Fnally, the vessel canddates connected to the optc nerve head were elmnated (as shown n Fg. 3 (e)) and the vessel canddates wth large or very small areas were elmnated. Fg. 3 (f) shows an example of a resultng mage. I I I*r I ç:c (a) (b) (c) p '. / (d) (e) (f) Fg. 3. Illustraton of the hemorrhage detecton processes. (a) Input mage. (b) Roughly detected hemorrhages and blood vessels. (c) Detals of detected hemorrhages and blood vessels. (d) Combnaton of (b) and (c). (e) Canddates connectng blood vessels and canddate wth large areas were elmnated. (f) Fnal mage of hemorrhage detecton. Proc. of SPIE Vol E-5

6 2.6 Elmnaton of ncorrectly detected vessels The mages of the retnal vessel nclude those reflected from the arteral wall. Further, the fundus mages were obscured due to cataract. Therefore, t was dffcult to detect all the vessels up to the end by usng the method proposed n secton 2.5. We can resolve such dffcultes by changng the threshold value proposed n secton 2.5. However, the hemorrhage canddates were connected to the vessel canddates. Therefore, our method could not separate the connected canddates nto hemorrhage and vessel canddates. The measures taken to resolve ths problem can be descrbed as follows. Frstly, the threshold value was selected n such a manner that the vessels could be contnuously detected. The centerlnes of the vessels were extracted by usng a thnnng technque. Subsequently, the centerlnes wth large areas were extracted n such a manner that the hemorrhage canddates could not be extracted. Fnally, the vessels extracted wth the centerlnes were elmnated n order to avod the vessels from beng ncorrectly detected. 2.7 Elmnaton of funcular shapes All the false postves on the vessels were not elmnated by the method proposed n secton 2.6. Hence, the remanng false postves were elmnated by evaluatng the length-to-wdth rato. The value of ths rato was small when the canddate was ncorrectly detected as a vessel. The detals of the evaluaton can be descrbed as follows. Frstly, the mnmum rectangular regon that surrounds the canddates was determned. The angle of the hemorrhage canddate was then determned by calculatng the moment so that the X and Y axes could be determned by usng the obtaned angle. Subsequently, bnarzed mages were projected on the X and Y axes. By examnng each of the projected mages, the postons Xa, Xb, Ya, and Yb were determned. In ths way, we could specfy the black and whte regons. Fnally, we could determne the length-to-wdth rato, denoted by LW, usng the followng equaton: mn( X LW = max( X a a X, Y b b a X, Y a Y b Y b ) ) (5) where mn( Xa Xb, Ya Yb ) s the mean value of the wdth, and max( Xa Xb, Ya Yb ) s the mean value of the length. 2.8 Elmnaton of false postves by feature analyss The mnmum rectangular regon that surrounds the hemorrhage canddates extends beyond fve pxels n each drecton along the X and Y axes. Next, the average pxel values nsde and outsde the canddate regon were calculated. Further, the contrast was determned by evaluatng the rato between the two average values. Furthermore, we proposed a cascade classfcaton process. At frst, the false postves were elmnated by a rule-based method usng 45 features. Typcal false postves were elmnated n ths step. We extracted the followng 15 features from the rectangular regons: 12 features calculated from the co-occurrence matrx [11], two features based on gray-level dfference statstcs [12], and one feature determned by the extrema method [13]. The 12 features from the co-occurrence matrx were (1) angular second moment, (2) contrast, (3) correlaton, (4) sum of squares, (5) nverse dfference moment, (6) sum average, (7) sum varance, (8) sum entropy, (9) entropy, (10) dfference varance, (11) dfference entropy, and (12) nformaton measurements for correlaton. The two features based on gray-level dfference statstcs were (a) angular second moment and (b) mean. The mnmum rectangular regon that surrounds the canddates was determned. These features were calculated n the rectangular regons n the three grey-level mages, whch comprsed red, green, and blue-bt mages. Fnally, the false postves were elmnated by employng the dscemment machnes and usng Mahalanobs dstances [14]. It was used suggested three Mahalanobs dstances by usng above 15 features from the rectangular regons n the three grey level mages. 3. RESULTS AND DISCUSSIONS The contrast of hemorrhages n the mages was enhanced. The contrast of the processed mage (as shown n Fg. 4 (c)) was hgher than that of the orgnal mage (as shown n Fg. 4 (b)). Moreover, the color of the processed mage was Proc. of SPIE Vol E-6

7 (a) (b) (c) (d) (e) Fg. 4. Process of hemorrhage enhancement. (a) Orgnal mage. (b) Enlarged mage of the regon ndcated by the whte rectangle n (a). Black arrow shows the hemorrhage. (c) Enhanced mage of (b). (d) Enlarged mage of the regon ndcated by the gray rectangle n (a). Black arrow shows the laser mark. (e) Enhanced mage of (d). standardzed by the hstogram extended method. However, f the fundus mage has some laser marks, the marks are changed blghter and more obscure than the orgnal color pxel (as shown n Fg.4 (d) and (e)). We set some parameters expermentally by usng 20 fundus mages wth hemorrhage. The senstvty was 95% (19/20). To elmnate the ncorrectly detected hemorrhages, we used 45 calculated features. One hundred and twelve hemorrhages were detected wth 630 false postves n 20 fundus mages. Table 1 shows the number of false postves elmnated by usng the rule-based method. In Table 1, the most effectve grey-scale mage s the green-bt mage. Moreover, the most vald feature was the nformaton measure of correlaton from the co-occurrence matrx for the green-bt mage, whch could elmnate 74 false postves (12%) by the rule-based method. However, four features n the green-bt mage could not elmnate one for false postve. When we used 45 features for the elmnaton of false postves, 166 false postves (26%) were elmnated wthout the loss of a true postve. Then, we constructed a dscemment machne by usng Mahalanobs dstances; we used 45 features for each greyscale mage (as shown n Table 2). When we constructed a dscemment machne by usng 112 true postves and 630 false postves, 117 false postves (19%) were elmnated. Table 2 shows that the most effectve mage s the blue-bt one, though Table 1 showed the most effectve mage was green-bt one. We thnk that the worst features that were neffectve had a negatve nfluence on the dscemment machne wth regard to the green-bt mage. Subsequently, 219 false postves (35%) were elmnated usng a rule-based method and three dscemment machnes by Mahalanobs dstances. Fnally, to evaluate our method of detectng hemorrhages, we examned 125 fundus mages; hemorrhages were detected n 35 mages, and no abnormal cases were detected n the remanng 90 mages. By usng our scheme, we Proc. of SPIE Vol E-7

8 succeeded n obtanng satsfactory results wth a senstvty of 80% (28/35) when the specfcty was 88% (79/90). Our scheme could not detect any hemorrhages. The man reason was that hemorrhages that touched the blood vessel were undetectable. Our algorthm was not able to separate the blood vessel regons and hemorrhage regons, and therefore such hemorrhages were removed together wth the blood vessels (dscussed n secton 2.6). Table 1. The false postves were elmnated by 45 rule-based methods. Features Red Green Blue Extrema CM: Angular second moment Contrast Correlaton Sum of squares Inverse dfference moment Sum average Sum varance Sum entropy Entropy Dfference varance Dfference entropy Informaton measurements GD: Angular second moment Mean Subtotal Total 166 CM: co-occurrence matrx, GD: gray-level dfference statstcs Table 2. The false postves were elmnated by three dscemment machnes usng Mahalanobs dstances. Red Green Blue Dscemment machne Three dscemment machnes combned Forty fve rule-based methods and Three dscemment machnes combned CONCLUSION In ths study, a new scheme for automatcally detectng hemorrhages s presented by usng dgtzed noncontrast fundus mages as an example. Ths scheme can be appled to the computer-aded dagnoss (CAD) system for dagnosng eye dseases. The results of the prelmnary testng showed a desrable consstency wth those obtaned from the proposed scheme. It was demonstrated that the algorthm detected abnormaltes wth hgh accuracy and relablty. The result of the ntal work on fundus mages clarfed that the effcency and accuracy of the dagnoss of DR was consderably mproved. The results of ths study wll be sent to ophthalmologsts for further evaluaton. The effcency and accuracy of the dagnoss of DR was mproved due to the detecton of hemorrhages wth a hgh accuracy. The applcaton of the proposed scheme to fundus mages enhances the CAD system performance for detectng hemorrhages n fundus mages. Proc. of SPIE Vol E-8

9 We have been attemptng to develop a synthetc fundus CAD system [15 22]. We reported methods of detectng abnormal blood vessels to help n the dagnoss of hypertensve retnopathy [15 18]. Moreover, we proposed a method of detectng retnal nerve fber layer defects (NFLD) [19] and a method of calculatng the cup to dsc rato (C/D rato) [20] to help n the dagnoss of glaucoma. In addton, we proposed a technque to obtan the depth value from the stereo mage par of a retnal fundus for the 3-D reconstructon of the optc nerve head [21, 22]. In the future, the ntegrated analyss scheme wll be further mproved and more clncal cases wll be reported for evaluatng ts accuracy. The technques employed n our system wll help n mprovng dagnostc accuracy as well as n reducng the workload of ophthalmologsts n the future. ACKNOWLEDGMENTS The authors thank T. Yamamoto, A. Fujta, Y. Mzukusa, T. Suzuk T. Kuneda, K. Sugo, N. Kajma, M. Okamoto, H. Mutoh, and H. Nonogak for ther sgnfcant contrbutons to ths study. Ths work was supported n part by Knowledge-based Clusters from the MEXT, Japan. REFERENCES 1. Health and Welfare Statstcs Assocaton, Journal of Health Welfare Stat., 51, , M. Nemejer, B. V. Gnneken, J. Staal, M. S. Suttorp-Schulten, and M. D. Abramoff, Automatc detecton of red lesons n dgtal color fundus photographs, IEEE Transactons on Medcal Imagng, 24 (5), , A. J. Framea, P. E. Undrll, M. J. Cree, J. A. Olson, K. C. McHardy, P. F. Sharp, and J. V. Forrester, A comparson of computer based classfcaton methods appled to the detecton of mcroaneurysms n ophthalmc fluorescen angograms, Computers n Bology and Medcne, 28 (3), , A. D. Flemng, S. Phlp, K. A. Goatman, J. A. Olson, and P. F. Sharp, Automated mcroaneurysm detecton usng local contrast normalzaton and local vessel detecton, IEEE Transactons on Medcal Imagng, 25 (9), , C. Serrano, B. Acha, and S. Revuelto, 2D adaptve flterng and regon growng algorthm for the detecton of mcroaneurysms, Proceedngs of SPIE Medcal Imagng 2007: Image Processng, 5370, , C. Snthanayothn, J. F. Boyce, T. H. Wllamson, H. L. Cook, E. Menshan, S. Lal, and D. Usher, Automated detecton of dabetc retnopathy on dgtal fundus mages, Dabetc UK Dabetc Medcne, 19 (1), , D. Usher, M. Dumskyj, M. Hmaga, T. H. Wllamson, S. Nussey, and J. F. Boyce, Automated detecton of dabetc retnopathy n dgtal retnal mages: a tool for dabetc retnopathy screenng, Dabetc UK Dabetc Medcne, 21 (1), 84 90, H. Nagayosh Y. Hramatsu, T. Kagehro, Y. Mzuno, M. Hmaga, H. Sakou, S. Sato, H. Fukushma, and S. Kato, Detecton of lesons from fundus mages for dagnoss of dabetc retnopathy, IEICE Techncal Report, 105 (64), 61 66, W. Doyle, Operaton useful for smlarty-nvarant pattern recognton, Journal of Assocaton for Computng Machnery, 9 (2), , Y. Hatanaka, T. Nakagawa, Y. Hayash, A. Fujta, Y. Mzukusa, M. Kakogawa, K. Kawase, T. Hara, and H. Fujta, CAD scheme for detecton of hemorrhages and exudates n ocular fundus mages, Proceedngs of SPIE Medcal Imagng 2007: Computer-aded Dagnoss, 6514, 65142M M-8, R. M. Haralck, Statstcal and structural approaches to texture, Proceedng of the IEEE, 67 (5), , J. S. Weszka, C. R. Dyer, and A. Rosenfeld, A comparatve study of texture measures for terran classfcaton, IEEE Transactons on Systems, Man, and Cybernetcs, SMC-6 (4), , O. R. Mtchell, C. R. Myers, and W. Boyne, A max-mn measure for mage texture analyss, IEEE Transactons on Computers, C-2 (4), , M. Iwamura, S. Omach and H. Aso, Character recognton wth Mahalanobs dstance based on between-cluster nformaton, IEICE Techncal Report, 98 (490), 49 54, Y. Hatanaka, T. Hara, H. Fujta, M. Aoyama, H. Uchda, and T. Yamamoto, Automatc dstrbuton and shape analyss of blood vessels on retnal mages, Proceedngs of SPIE Medcal Imagng 2007: Image Processng, 5370, , Proc. of SPIE Vol E-9

10 16. Y. Hatanaka, X. Zhou, T. Hara, H. Fujta, Y. Hayash A. Aoyama, and T. Yamamoto, Automated detecton algorthm for abnormal vessels on retnal fundus mages, Proceedngs of the 10th Internatonal Conference on Vrtual Systems and MultMeda (VSMM2004), , Y. Hatanaka, T. Nakagawa, Y. Hayash A. Aoyama, X. Zhou, T. Hara, H. Fujta, Y. Mzukusa, A. Fujta, and M. Kakogawa, Automated detecton algorthm for arterolar narrowng on fundus mages, Proceedngs of the 2005 IEEE Engneerng n Medcne and Bology 27th Annual Conference, paper#291, R. Takahash Y. Hatanaka, T. Nakagawa, Y. Hayash A. Aoyama, Y. Mzukusa, A. Fujta, M. Kakogawa, T. Hara, and H. Fujta, Automated analyss of blood vessel ntersectons n retnal mages for dagnoss of hypertenson, Medcal Imagng Technology, 24 (4), , Y. Hayash T. Nakagawa, Y. Hatanaka, A. Aoyama, Y. Mzukusa, A. Fujta, M. Kakogawa, T. Hara, H. Fujta, and T. Yamamoto, Detecton of retnal nerve fber layer defects n retnal fundus mages usng Gabor flterng, Proceedngs of SPIE Medcal Imagng 2007: Computer-aded Dagnoss, 6514, 65142Z Z-8, Y. Hatanaka, Y. Hayash T. Nakagawa, A. Aoyama, X. Zhou, T. Hara, H. Fujta, Y. Mzukusa, A. Fujta, and M. Kakogoawa, Development of Computer-aded Dagnoss System for Fundus Images, 8th Internatonal Conference on Medcal Image Computng and Computer-asssted Interventon: Short Paper, Oct., 2005, T. Nakagawa, Y. Hayash Y. Hatanaka, A. Aoyama, T. Hara, M. Kakogawa, H. Fujta, and T. Yamamoto, Comparson of the depth of an optc nerve head obtaned usng stereo retnal mages and HRT, Proceedngs of SPIE Medcal Imagng 2007: Physology, Functon, and Structure from Medcal Images, 6511, 65112M M-9, T. Nakagawa, Y. Hayash Y. Hatanaka, A. Aoyama, T. Hara, A. Fujta, M. Kakogawa, H. Fujta, and T. Yamamoto, Three-dmensonal reconstructon of optc nerve head from stereo fundus mages and ts quanttatve estmaton, Proceedngs of 29th IEEE Engneerng n Medcne and Bology Conference Management System Annual Internatonal Conference, , Proc. of SPIE Vol E-10