Originl Artile www.jmss.mui..ir Anlysis of Fundus Fluoresein Angiogrm Bsed on the Hessin Mtrix of Diretionl Curvelet Su nds nd Distne Regulrized Level Set Evolution Asieh Soltnipour 1, Seed Sdri 1,, Hossein Rni, Mohmmd Rez Akhlghi 3 1 Deprtment of Eletril nd Computer Engineering, Isfhn University of Tehnology, Isfhn, Irn, Deprtment of Biomedil Engineering, Medil Imge nd Signl Proessing Reserh Center, Isfhn University of Medil Sienes, Isfhn, Irn, 3 Deprtment of Ophthlmology, Isfhn University of Medil Sienes, Isfhn, Irn Sumission: 1 05 015 Aepted: 08 07 015 ABSTRACT This pper presents new proedure for utomti extrtion of the lood vessels nd opti disk (OD) in fundus fluoresein ngiogrm (FFA). In order to extrt lood vessel enterlines, the lgorithm of vessel extrtion strts with the nlysis of diretionl imges resulting from su nds of fst disrete urvelet trnsform (FDCT) in the similr diretions nd different sles. For this purpose, eh diretionl imge is proessed y using informtion of the first order derivtive nd eigenvlues otined from the Hessin mtrix. The finl vessel segmenttion is otined using simple region growing lgorithm itertively, whih merges enterline imges with the ontents of imges resulting from modified top ht trnsform followed y it plne sliing. After extrting lood vessels from FFA imge, ndidte regions for OD re enhned y removing lood vessels from the FFA imge, using multi struture elements morphology, nd modifition of FDCT oeffiients. Then, nny edge detetor nd Hough trnsform re pplied to the reonstruted imge to extrt the oundry of ndidte regions. At the next step, the informtion of the min r of the retinl vessels surrounding the OD region is used to extrt the tul lotion of the OD. Finlly, the OD oundry is deteted y pplying distne regulrized level set evolution. The proposed method ws tested on the FFA imges from ngiogrphy unit of Isfhn Feiz Hospitl, ontining 70 FFA imges from different dieti retinopthy stges. The experimentl results show the ury more thn 93% for vessel segmenttion nd more thn 87% for OD oundry extrtion. Key words: Algorithms, omputer-ssisted, dieti retinopthy, fluoresein ngiogrphy, Hessin mtrix, Imge proessing, level set method, retinl vessels INTRODUCTION Dieti retinopthy (DR) is usully leding use of lindness nd vision redutions ll over the world. A vision sreening n e used to detet retinopthy for people who ll re t risk for eye diseses. Detetion nd loliztion of min strutures in the retinl imges, like lood vessels nd opti disk (OD) region, hve n importnt role in dignosis system of DR nd some other diseses. Mnully extrtions of these strutures re very time onsuming in retinl imges nd its ury (ACC) depends on the user skill level. For this purpose, mny methods hve een proposed for vessel segmenttion nd OD loliztion. A few methods of vessel segmenttion nd OD detetions re explined in the following: Retinl vessel segmenttion nd desription of hrteristis of the lood vessels, suh s length, width, rnhing ptterns, nd ngles n e used s n importnt tools for evlution of the retinl disorders, mul nd OD detetion, dignosis, nd sreening of vrious rdiovsulr nd ophthlmologi diseses, nd lser surgery. [1-3] Aording to Frz, [1] vessel segmenttion methods n e done in five min tegories: (1) Pttern reognition, () mthed filtering, (3) vessel trking/tring, (4) mthemtil morphology, nd (5) multi sle pprohes. In eh tegory, severl methods re reported in the study y Frz. [1] Pttern reognition tehniques n e divided in two tegories: (1) supervised methods, nd () unsupervised methods. An pproh sed on the k propgtion neurl network is desried for the segmenttion of lood vessels in ngiogrphy. [4] Two dimensionl (D) Gor wvelet nd supervised lssifition is used for retinl vessel segmenttion. [5] The feture vetors re formed y the pixels intensity nd D Gor wvelet oeffiients. An lgorithm sed on feeding seven dimensionl feture vetor omposed of gry level nd moment invrint sed fetures re given to supervised neurl network, to extrt the vessels. [6] The Address for orrespondene: Dr. Hossein Rni, Deprtment of Biomedil Engineering, Medil Imge nd Signl Proessing Reserh Center, Isfhn University of Medil Sienes, Isfhn, Irn. E mil: h_rni@med.mui..ir Vol 5 Issue 3 Jul-Sep 015 141 Journl of Medil Signls & Sensors
Soltnipour, et l.: Automti nlysis of fundus fluoresein ngiogrm pproh n e onsidered s mthed filter method. [7 9] A D liner kernel with Gussin profile is introdued for vessel detetion. [7] Sine, vessel my hve ny ngle, the kernel is rotted in 1 different diretions nd mximum response in eh pixel is retined. Amplitude modified seond order Gussin filter hs een proposed for lood vessel segmenttion. [8] Also, D Gussin mthed filter is used for vessel enhnement, nd then neurl network is pplied to vessel detetion. [9] A trking method with Gussin nd Klmn filters for vessel segmenttion in retinl imges is proposed. [10] The seond order mthed filter is used for enterline estimtion nd then trking proess is strted. The Klmn filter is pplied for the estimtion of next vessel lotion. Also, modulr supervised method for vessel segmenttion is proposed. [11] The imge kground is normlized for nonuniform intensity vritions using sle spe theory nd supervised optimiztion proedure is pplied to determine the optiml sle. OD region loliztion is n importnt lndmrk feture in retinl imge nlysis, nd its dimeter is usully used s referene for fove region detetion. OD is usully hrterized s righter omponent thn the rest of oulr fundus from whih the lood vessels nd opti nerves emerge from it. Reently, mny methods re proposed for the OD segmenttion. Priniple omponent nlysis (PCA) is used to extrt OD even for imges with right lesions. [1] In this method, trining set using the rightest pixels re produed s OD ndidte regions nd then, PCA is inorported to projet new imge to disk spe. Hough trnsform method is proposed to lote OD. [13] In this method, irulr region is found y first isolting the rightest re in the imge using morphologil opertors nd then, Hough trnsform is implemented to detet the irulr re fetures within the grdient imge resulting from region of interest (ROI). A geometril diretionl pttern of the retinl vsulr system is used to emed informtion on the OD lotion whih is the point of onvergene of the vessels. [14] OD is deteted using vessels diretion mthed filter from normlized digitl fundus imges. [15] At first, the retinl vessels re deteted y stndrd D Gussin mthed filter. Then, the deteted vessels re thinned nd filtered using lol intensity to represent OD enter ndidtes. OD region is deteted y the omintion of fst disrete urvelet trnsform (FDCT) nd level set deformle model in olor imges. [16] In this method, OD region ndidte is extrted using modified urvelet oeffiients nd nny edge detetor nd then, the oundry of OD is extrted y level set deformle model. This pper presents n utomti system to detet min strutures, suh s OD nd vsulr tree, in the fundus fluoresein ngiogrm (FFA) imges. The overview of the proposed method is explined in Setion II. The proposed method for vessel segmenttion tht is modified version of proposed method whihis desried in Setion III. [17] This method ontins three min phses: (1) Preproessing, () enterline extrtion using informtion out the first order derivtive nd eigenvlues otined from the Hessin mtrix of FDCT su nds, nd (3) tree vessel segmenttion using region growing lgorithm. After the segmenttion of lood vessels from the FFA imges, OD oundry is extrted using four phse lgorithm. This method is desried in Setion IV nd inludes: (1) Removing vessels, () ndidte OD regions detetion using nny edge detetor nd Hough trnsform, (3) deteting tul lotion of OD, using the informtion of min r of retinl vessels round the OD region, nd (4) extrting OD oundry sed on the distne regulrized level set evolution (DRLSE) model. After explining the proposed method in this pper, the experimentl results on the FFA imges of the Isfhn entrl eye lini nd the imges of DRIVE dtse re presented in Setion V. Finlly, Setion VI ontins the min onlusions of this pper. OVERVIEW OF THE PROPOSED METHOD The funtionl lok digrm of the proposed method in this pper for vessel detetion nd OD oundry extrtion is shown in Figure 1. Figure 1: Funtionl lok digrm of proposed method. The proposed method for extrtion of the lood vessels nd of the opti disk lotion is shown in the left nd right olumns respetively 14 Vol 5 Issue 3 Jul-Sep 015 Journl of Medil Signls & Sensors
Soltnipour, et l.: Automti nlysis of fundus fluoresein ngiogrm The left olumn of Figure 1 shows the proposed lgorithm for segmenttion of the lood vessels, where involves three si proessing phses: 1. Preproessing for redution of kground intensity vrition nd ontrst enhnement of thin vessels.. Extrtion of vessel enterlines, whih this phse involves three su steps s follows: First step: Mking set of diretionl imges nd extrting initil ndidte enterlines sed on the informtion of the first order derivtive nd the eigenvlues from the Hessin mtrix Seond step: Evlution of the enterline segments Third step: Connetion of the diretionl imges resulting from two previous steps nd removl of the flse edges. 3. Vsulr tree segmenttion, for omplete segmenttion of the tree vessels, whih omines the vessel enterlines with the set of the imges resulting from modified top ht trnsform followed y it plne sliing. The right olumn of Figure 1 shows the proposed lgorithm for extrtion of OD oundry, whih inludes four min proessing phses: 1. Contrst enhnement of the OD region through removing of the lood vessels y using sequene of diltion nd erosion morphologil opertors.. Deteting ndidte regions for OD, where this phse inludes two su steps s follows: First step: Applying FDCT to the imge resulting from the ontrst enhnement of the OD region phse nd modifition of the urvelet oeffiients. Seond step: Applying nny edge detetor nd Hough trnsform on the reonstruted imge from the modified oeffiients urvelet. 3. Deteting onvergene point of vessels in order to find the tul lotion of OD. Aording to geometril struture of the lood vessels, min r of the vessels n e pproximted y two lines in diretions of 75 nd 105 nd the interept point of these two lines n e defined s onvergene point of the retinl vessels. The tul lotion of OD region is otined using evlution of the Euliden distne etween the enters of OD ndidte regions nd the onvergene point of the retinl vessels. 4. Extrting OD oundry sed on the DRLSE model. kground intensity vrition of the retinl imges mkes some of the hrteristi of retinl imges (e.g., retinl pillries) to eome hrdly visile whih ffets the outome of the vessel segmenttion. Therefore, the kground intensity vritions re redued y pplying nonliner diffusion filtering on the originl imge. [18]. Contrst enhnement of the thin vessels: To enhne ontrst of thin nd smll vessels, the imge resulting from nonliner diffusion filtering is proessed y sequene of modified top ht trnsform itertively, [19] using multi struture elements morphology. In this pper, the struturing elements with size 7 7 pixels re mde in 1 different diretions with resolution 15. The detil of the prodution of struturing elements is ville in Eq. (1), whih represents the modified top ht trnsform: [19] Top Ht I = I min(( I S) S;) I (1) Where, I is the output of the nonliner diffusion filtering to e proessed, S is represented for the multi struturing elements morphology nd re morphologil losing nd opening opertors respetively. Then, the set of the imges otining from the modified top ht trnsform y 1 multi struturing elements re omined. [19] Figure shows the results of this setion. Vessel Centerlines Extrtion Seletion of initil ndidte enterlines Sine, the retinl lood vessels hve rnge of different sizes nd lso n our in different diretions, the nlysis of set of the diretionl imges (nd their omintions) in different sizes of the lood vessels dimeter would e informtive. So, t first, set of the diretionl imges sed on su nds of FDCT is mde nd then eh of these diretionl imges is proessed in different sizes lood vessels dimeter for extrting initil ndidte enterlines s following: The detils of eh one of these phses re desried in the next setions. VESSEL SEGMENTATION Imge Preproessing The imge preproessing onsists of two min phses s follows: 1. Redution of the kground intensity vrition: The Vol 5 Issue 3 Jul-Sep 015 Figure : () Fundus fluoresein ngiogrm imge. () Output of nonliner diffusion filtering. () Output of modified top ht trnsforms 143 Journl of Medil Signls & Sensors
Soltnipour, et l.: Automti nlysis of fundus fluoresein ngiogrm FDCT is multi sle nd multi diretionl deomposition. [0] At first, FDCT is pplied on preproessed imge (resulting from Setion IIIA). Then, to reonstrut set of the diretionl imges from the FDCT oeffiients, nd ll oeffiients in speifi diretion in different sles is preserved, nd others re set to zero. In this pper, FDCT vi wrpping is pplied in 5 sles nd 16 diretions in the seond sles, whih mkes 3 diretions in 3 rd nd 4 th sles nd 64 diretions in 5 th sle. Figure 3 shows set of the shded diretionl wedges in the seond to fifth sles whih mke the first diretionl imge. Under this frmework, the numer of diretionl imges would e the hlf numer of diretionl su nds in the seond sle. So, diretionl imges with frequeny ontents [3π/4 (i 1)π/8 3π/4 iπ/8] for 1 i 8 re otined, tht is, eh one of the diretionl imges is vergely direted in the ngle defined y: ( À / 8 ( i 1) À / 8) + ( À / 8 ià / 8) θ( i) = o 1 i 8 Aording to Gussin profile of vessels nd redution of intensity vlues from vessel enterlines to vessel edges in the FFA imges, pixel from the diretionl imges elongs to vessel enterline, if its intensity vlue is greter thn threshold. So, we would hve eight imges I i, for 1 i 8, whih represents the finl diretionl imges resulting from this step. Figure 4 shows the diretionl imges I i, for1 i 8. To proess the informtion of the lood vessels t speifi diretion nd different sles of the lood vessels dimeter, eh one of the diretionl imges I i is onvolved with Gussin kernel G (x.y; s(j)) of vrine s (j) ording to Eq. (3). Sine, the dimeter of the lood vessels hnge logrithmilly, s(j) n e defined y Eq. (4). [1] Iis,() j (, x ys ; ( j)) = Ii(, xy) Gxys (, ; ()) j (3) ( j 1) ( log( ) log ( ) ) S( j)= exp log ( )+ (4) ( Ns 1) () In Eq. (3) nd (4), 1 i 8, 1 i N s, s(j) is length of the Gussin kernel, N s is the numer of sles nd, re minimum nd mximum rdius of the vessels. In this pper sed on the dtset,, nd N s re onsidered s 0.4,, nd 5 respetively. The Gussin kernel is defined s: 1 Gxys (, ; ( j)) = π s() j e x + y s ( j) So, I i,s(j) in Eq. (3), ontins strutures with hrteristi length <s(j), whih re direted in ngle q (i). Also, derivtives of diretionl imge n e pproximted y liner onvolution of the diretionl imge with sle normlized derivtives of the Gussin kernel s following: n n n I (, x y) = I ( xy, ) s() j Gxys (, ; ( j)) (6) is, ( j) i Where, n is the order of the derivtive. In this pper, to extrt initil ndidte enterlines, eh pixel of the diretionl imges I i,s(j) is proessed using the informtion of the eigenvlues of Hessin mtrix nd the first order derivtive of I i,s(j). On the other hnd, to lulte eigenvlues of the Hessin mtrix with less sensitivity noise, the vessels in eh diretionl imge I i must e ligned with the x xis. [] For this purpose, oordinte xis in eh diretionl imge I i is rotted with q(i). [] Under this frmework, the nlysis of the first order derivtive of I i,s(j) nd the eigenvlues of Hessin mtrix is done. The pixel elongs to enterline if the following riteri re stisfied: Evlution of the first order derivtive of the diretionl imges For this purpose, the first order derivtive of the diretionl imges I i,s(j) is lulted long x xis s: I dir (, x y ) is,( j) Gxys (, ; ( j)) = Idir (, x y) s( j) i x x Then, eh pixel of resulted imges from Eq. (7) is investigted for the hnge of signs of the two its neighoring pixels on perpendiulr diretion to the diretion of the orresponding diretionl imge. (5) (7) Eigenvlue nlysis For this purpose, the Hessin mtrix for eh pixel of diretionl imges I i,s(j) is defined s: Idir x HiS, ( j) = S () j Idir x y I is,( j) diri, s( j) is,( j) xy I dir is,( j ) y (8) Figure 3: Diretionl wedges in speifi diretion in seond to fifth sles for mking first diretionl imge In Eq. (8), 1 i 8 nd 1 1 j 5. Also, eh one of the elements of the Hessin mtrix is lulted y Eq. (6). 144 Vol 5 Issue 3 Jul-Sep 015 Journl of Medil Signls & Sensors
Soltnipour, et l.: Automti nlysis of fundus fluoresein ngiogrm Assuming the eigenvlues, l 1 nd l, for eh pixel of the diretionl imges I i,s(j), pixel from the imge elongs to vessel enterline, if the lrgest eigenvlue from the Hessin mtrix is negtive. So, eh diretionl imge I i,s(j) is serhed for the negtive eigenvlue with the mximum mgnitude. At the next step, the histogrm of the negtive eigenvlues with the mximum mgnitude is lulted for eh diretionl imge I i,s(j). The histogrm is utomtilly divided into two prts using Otsu threshold lgorithm. Eh pixel with the eigenvlue greter thn Otsu threshold elongs to n initil enterline point. For those pixels, whih re elow the Otsu threshold, their mens nd stndrd vlues re lulted nd eh pixel whih is greter thn the sum of men nd stndrd vlue is ssigned to n initil enterline point. [3] Evlution of enterline ndidte segments Evlution of enterline seleted segments is done in three steps: At first step, for eh pixel from diretionl imges resulting from previous setion, the intensity nd Euliden norm of eigenvlues re multiplied together. Then, the mximum vlue of this feture is lulted for eh pixel in speifi diretion on ll sles of Gussin kernel. Figure 5 illustrtes the diretionl imges resulting from this step At seond step, the diretionl imges resulting from the first step re omined together ording to first olumn of Tle 1. Therefore ording to third olumn of Tle 1, there will e just four min diretions for the imges. Figure 6 shows four diretionl imges otined from Tle 1 At third step, the intensity feture of eh ndidte segment in four diretionl imges [Tle 1] is lulted using geometri men etween the men nd mximum intensity vlues of the ndidte segment. [3] Then, the histogrm of ll ndidte segments intensities in eh diretionl imge is utomtilly divided into two prts using Otsu threshold lgorithm. Then, for those pixels whih re elow the Otsu threshold, their minimum, mximum nd stndrd devition re d e f Figure 4: ( h) The figure shows set of diretionl imges, I i for 1 i 8, whih is vergely direted in the ngles 33.75, 11.5, 168.75, 146.5, 13.75, 101.5, 78.75 nd 56.5 respetively g h d e f Figure 5: ( h) Set of diretionl imges resulting from evlution of two fetures intensity nd sum of squres of the eigenvlues on the sles of Gussin kernel g h Vol 5 Issue 3 Jul-Sep 015 145 Journl of Medil Signls & Sensors
Soltnipour, et l.: Automti nlysis of fundus fluoresein ngiogrm Tle 1: Angels of diretionl imges Diretionl imge of Figure 5 Angle of diretionl imge (Ɵ i ) Figure 6: (-d) Four diretionl imges resulting from Tle 1 (digonl 45, horizontl, digonl 135 nd vertil) lulted nd two thresholds T 1 nd T re defined s geometri men etween the minimum nd stndrd vlue nd geometri men etween the mximum nd stndrd vlue, respetively. The ndidte segments with intensity vlue greter thn T re onsidered s vessel enterlines nd ndidte segments with intensity vlue smller thn T 1 re removed from the imge. In the next setion, the ndidte segments with the intensity feture greter thn T 1 nd smller thn T re proessed in order to otining the tul enterline segments. Connetion of diretionl imges nd flse edge removl The diretionl imges resulting from previous setion re onneted together in two phses: 1. All the enterline segments with the intensity vlue greter thn T of the orresponding diretionl imge re jointed together s shown in Figure 7. The otined imge ontins the min nd lrge vessels of the originl imge. The enterline segments with the intensity vlue greter thn T 1 nd smller thn T re jointed together s shown in Figure 8. The otined imge ontins thin d Approximted ngle () 11.5 0 () 168.75 () 33.75 45 (h) 56.5 (f) 101.5 90 (g) 78.75 (d) 146.5 135 (e) 13.75 nd smll vessels nd lso few nonvessel strutures of the originl imge. The nonvessel strutures in the resulted imge re removed in the following min steps: Miro neurysms (MAs) re the first sign of DR with the similr intensities of the vessels in the FFA imges. These nonvessel strutures n e oserved s more right spots lose to thin nd smll vessels. To remove these nonvessel strutures from the imge, t first the ndidte segments with length smller thn n experimentlly determined threshold re onsidered. Length of the ndidte segment is lulted y mximum numer of its pixels in vertil or horizontl diretion. Then, mximum nd minimum intensities of every ndidte segment re onsidered from the resulted imge in Setion IIIA. The pixel with mximum intensity is removed from the imge, if differene etween mximum nd minimum intensities is greter thn T = 50. This proedure is repeted until no more pixels re removed. In this step, eh pixel from ndidte segments with length vlue smller thn T = 5 is leled s rnh, end or urve point y the numer of 8 djned pixels ording to the following rule: n8 = 1 end point n8 = urve point n8 3 rnh point. Sine, MAs n e oserved s righter spots lose to thin nd smll vessels, pixels leled s rnh-point re removed from the imge. This is euse, they my e the result of MAs. Finlly, two imges resulting from this setion re jointed together into inry imge nd the onneted omponents with the re smller thn T = 9 re removed from the imge. Figure 9 shows vessel enterlines imge. Vsulr Tree Detetion Till now we hve otined the enterline of vessels. In order to extrt the finl vsulr tree the following region growing lgorithm is pplied. Aggregted imges genertion In this step, the originl imge is proessed y pplying sequene of the modified top ht trnsform y the irulr struturing elements ording to Eq. (9), where S nd S o re respetively represented the struturing elements for losing ( ) nd opening ( ) opertors. [] top ht() I = I min(( I S ) S ;) I (9) In Eq. (9), the morphologil lose opertor, whih is diltion followed y erosion, is pplied to generte smooth version of the originl imge nd erse the vrition of kground. A irulr struturing element of rdius 1 pixel o 146 Vol 5 Issue 3 Jul-Sep 015 Journl of Medil Signls & Sensors
Soltnipour, et l.: Automti nlysis of fundus fluoresein ngiogrm d Figure 7: ( d) Centerline segments with the intensity feture greter thn threshold T (digonl 45, horizontl, digonl 135 nd vertil). (e) Imge resulting from onnetion of ndidte segments in ( d) e d Figure 8: ( d) Centerline segments with the intensity feture greter thn threshold T 1 nd smller thn T (digonl 45, horizontl, digonl 135 nd vertil). (e) Imge resulting from onnetion of diretionl imge in ( d) e is onsidered for losing opertor. The opening opertor, whih is n erosion followed y diltion, is pplied to remove those vessels from the imge whih re smller in size thn the defined struture element. Sine retinl lood vessels hve rnge of different sizes, the irulr struturing elements with vrious rdius, 1,, 3, nd 4 pixels re onsidered for opening opertor. Eh of the four finl imges resulting from modified top ht trnsform in Eq. (9) represents the ojets of imge with size smller Vol 5 Issue 3 Jul-Sep 015 thn the struturing element used for the orresponding opening opertor. Then, to reonstrut potentil segments of vessel in eh of four diretionl imges resulting from Eq. (9), eh one of these imges is represented in the form of it plnes, rnging from it plne 1 to it plne 8. Bit plne sliing n e used s useful tool for seprting different prts of n imge. Tle shows it plns onsidered for eh one of 147 Journl of Medil Signls & Sensors
Soltnipour, et l.: Automti nlysis of fundus fluoresein ngiogrm the four imges resulting from Eq. (9). The other it plns result more detils of imges nd n e ppered s noise in the finl imge of vsulr tree. Finlly, the summtion of it plns onsidered for eh one of the resulted imges from Eq. (9) is lulted ording to Tle. Figure 10 shows four imges resulting from nlysis of it plns sliing. Four imges otined from this setion, re onsidered s ggregted imges for region growing lgorithm. Vessel filling The finl imge of the vsulr tree is otined y omining vessel enterlines resulted from Setion IIIB with the four imges resulting from the nlysis of it plns sliing in previous phse using the simple region growing lgorithm s shown in Figure 11. Aording to Figure 11, in the first itertion, the vessel enterlines imge is used s seed points nd the reonstruted imge from top ht trnsform with the smllest struturing element for opening opertor is used s n ggregted imge for simple region growing lgorithm. In eh of the susequene three itertions, ording to Figure 11, the reonstruted imges from top ht trnsform with inresing size of struturing element re used s seed points nd outputs of the previous region growing steps re used s ggregted imge. The finl vsulr tree segmenttion is otined fter implementtion of the region growing lgorithm in the fourth itertion. Figure 1 shows vessel enterlines nd finl vessel segmenttion using the proposed method. Tle : Bit plns onsidered for reonstrution imges resulting from top-ht trnsform Rdius of struturing element S O Bit plns used 1 pixel 3,4,5,6 nd 7 pixel 5,6 nd 7 3 pixel 5,6 nd 7 4 pixel 6,7 nd 8 OPTIC DISK BOUNDARY EXTRACTION Opti Disk Contrst Enhnement OD region extrtion n e used s mrker to determine fove in retinl imge nd lso, hs n importnt role in DR dignosis. OD in the FFA imges is hrterized s right disk with intensity similr vessels. Given tht, the vessels nd OD re oth right strutures in the FFA imges. So, in order to extrt OD oundry, t first lood vessels re removed from retinl imge. Sine, the retinl lood vessels n our in different diretions; to remove lood vessels, the FFA imge is proessed y sequene of the diltion nd erosion opertors, defined y Eq. (10), using the multi struturing elements. As mentioned in Setion IIIA, struturing elements with size 7 7 pixels re onsidered in 1 different diretions with resolution 15. In Eq. (10), I is the originl imge, S is multi struturing element \oplus nd \ominus re diltion nd erosion opertors respetively. F = (( I S) S) (10) Eh output imge from Eq. (10) shows vessels in the sme diretion of the orresponding struturing element. So, the minimum response in eh pixel is lulted for 1 imges resulting from Eq. (10) for vessels removl. At next step, to redue OD flututions, the imge resulting from vessel removl lgorithm is proessed y diltion nd erosion morphologil opertors using disk struturing element with rdius of 5 pixels. [16] Figure 13 shows the results of this setion for OD ontrst enhnement. Identifition of Opti Disk Cndidte Regions In this setion, the ndidte regions for OD re extrted in two min steps: Step 1: FDCUT is multi diretionl deomposition pproh, whih its lrge oeffiients orrespond to the right ojets in the imge. [16] OD in the imge resulting from Setion IVA Figure 9: () Figure 7e. () Figure 8e. () Deteted vessel enterlines Figure 10: ( d) The imges resulting from nlysis of it plns sliing d 148 Vol 5 Issue 3 Jul-Sep 015 Journl of Medil Signls & Sensors
Soltnipour, et l.: Automti nlysis of fundus fluoresein ngiogrm ppers s right ojet, regrdless of the kground intensity vrition. So, OD lotion n e pproximtely determined y trnsforming the enhned imge using FDCT vi wrpping nd modifying FDCT oeffiients y funtion D with the exponent of P whih is defined y Eq. (11). [16] D (x) = x p (11) In this pper, P = 15 is experimentlly seleted for modifying FDCT oeffiients. Then, the invers FDCT is pplied to modified oeffiients to distinguish OD ndidte regions from other regions of the imge. Figure 14 shows the results of reonstrution of the modified FDCT oeffiients for Figure 13. Step : In this step, nny edge detetor is pplied on the imge resulting from the previous step. The kground intensity vrition round OD region hs used un irulr strutures for OD ndidte regions. So, Hough trnsform is used for reonstruting OD ndidte regions resulting from nny edge detetor with the un irulr strutures in rnge of rdius. [1] Figure 15 shows the results of this step. Atul Lotion of Opti Disk Region Atul lotion of OD region is determined in three min steps: (1) Extrting the min r of the retinl vessels round OD region, () Determining vertex of the min r of the vessels in the retinl imges (onvergene point for retinl lood vessels), (3) Evluting the Euliden distne etween the enters of OD ndidte regions nd vertex of the min r of the retinl vessels. Extrting the min r of the vessels The min r of the retinl vessels round OD region is extrted ording to the phses: Step 1: Aording to struture of min r of the retinl vessels round OD region, the imge resulting from vessel extrtion lgorithm, [Figure 1] is proessed y opening opertor using the multi struturing elements with size 7 7 pixels whih re direted in ngles of 75 nd 105. Then, summtion of these two imges is onsidered for the next proesses. The results of this step re shown in Figure 16. Step : In this step, to determine min r of the retinl vessels, the length of vessel ndidte segments resulting from previous step is lulted y onsidering the numer of pixels in vertil diretion. Then, men nd stndrd vlue Figure 1: () Deteted vessel enterlines. () Finl vessel segmenttion Figure 11: The proposed method for vessel filling Figure 13: () Originl imge. () Opti disk ontrst enhnement Vol 5 Issue 3 Jul-Sep 015 Figure 14: () Opti disk ontrst enhnement. () The imge resulting from reonstrution of the modified fst disrete urvelet trnsform oeffiients 149 Journl of Medil Signls & Sensors
Soltnipour, et l.: Automti nlysis of fundus fluoresein ngiogrm of the distriution of length of vessel ndidte segments re lulted nd the ndidte segments with the length smller thn totl men nd stndrd vlue re removed from the imge. Afterwrd, the enter of mss of eh segment remining from previous step is lulted nd Euliden distne etween its enter of mss nd enter of mss of other segments is omputed. Then, pir of segments with the minimum Euliden distnes is onsidered nd men of the distriution of Euliden distnes of remined pirs is lulted. Finlly, only pir of segments with the Euliden distnes smller thn the men vlue is preserved nd the others re removed from the imge. So, the min r of the retinl vessels is extrted y implementtion of these two steps. Figure 17 shows the min r of the vessels round the OD region. Convergene point of the lood vessels determintion To determine vertex of min r of the vessels, the imge resulting from previous setion is proessed y pplying opening morphologil using two liner struturing elements with size 7 7 pixels whih re direted in ngles of 75 nd 105, s shown in Figure 18. Then, eh one of the two segments resulting from previous step n e pproximted y line onneting two endpoints of the orresponding segment [violet olor in Figure 18]. Finlly, interept point of these two lines n e defined s onvergene point of the retinl vessels [mrked y red olor in Figure 18]. Evlution of the opti disk ndidte regions To determine tul lotion of OD, t first the enter of eh one of the OD ndidte regions, resulting from Setion IVB, is onsidered. Then, the Euliden distne etween the enters of OD ndidte regions nd the onvergene point of the retinl vessels is omputed. Finlly, the ndidte region with the smllest Euliden distne is onsidered s the tul lotion of OD. Figure 19 shows the result of this setion. OD Boundry Segmenttion In this setion, OD region oundry is deteted sed on the DRLSE model whih is defined with potentil funtion suh tht the derived level set evolution hs unique forwrd nd kwrd (FAB) diffusion effet. [4] The detiled proedure of the DRLSE model is ville. [4] In this model, initil ontour is signifintly onverged towrd the ojet oundry. For this purpose, the imge resulted from Figure 16: ( nd ) The imges resulting from opening opertor y multi struturing elements with size 7 7 pixels in ngles of 75 nd 105, respetively. () Summtion of () nd () Figure 15: ( nd ) The imges resulting from nny edge detetor nd Hough trnsform, respetively Figure 17: () Vessels imge. () Min r of the vessels round opti disk region Figure 18: ( nd ) The imges resulting from proessing min r y opening opertor using the two liner struturing elements with size 7 7 pixels in ngle 75 nd 105, respetively. () Convergene point of the lood vessels 150 Vol 5 Issue 3 Jul-Sep 015 Journl of Medil Signls & Sensors
Soltnipour, et l.: Automti nlysis of fundus fluoresein ngiogrm Setion IVC is defined s initil level set ontour. The initil level set ontour is itertively onverged towrd oundry ojet y the minimiztion of the totl energy funtion: φ = + φ µ div( dp( φ) φ) λδε( φ) div( g + αgδε φ t φ ) ( ) (1) The first term on the right hnd side in Eq. (1) is the level set distne regulriztion, the seond term omputes the line integrl of the funtion g long the zero level set ontour of f nd this term is minimized while the zero level set ontour of f is onverged t the oundry ojet, nd the third term is defined for elerting of displement of zero level set ontour towrd oundry ojet in the level set evolution proess. The prmeters used in Eq. (1) re defined s follows: is the Lplin opertor nd d e (x) is the smooth version of Dir delt funtion with e = 1.5 tht is defined s: 1 x ε δ ( ) [ + os( )], x ε x = ε 1 À ε (13) 0, x > ε g is the inditor funtion nd defined y: 1 g = + 1 G * I σ (14) In Eq. (14) imge I is lulted y the summtion of the imge resulted from Setion IVA nd the imge resulted from the modified FDCT oeffiients in Setion IVB, in order to get inditor funtion with more ACC. G s I is the imge resulting from the onvolution with Gussin kernel with stndrd devition s. l > 0 nd R re the oeffiients of the energy funtionl. Note tht, if the initil ontour is loted inside the ojet, the oeffiient should e negtive nd otherwise should e positive. m > 0 is defined to ontrol the effet of penlizing the devition of f from signed distne funtion. of orresponding ojet. Figure 0 shows the result of this step for =, l = 10, m = 0.,s = 1 fter 00 itertions. EXPERIMENTAL RESULTS Vessel Segmenttion Method Our proposed lgorithm for vessel segmenttion ws tested on the FFA imges of size 576 70 pixels from ngiogrphy unit of Isfhn Feiz Hospitl whih onsist 30 imges for norml retins nd 40 imges of norml retins. These imges n e downloded from https://sites.google.om/ site/hosseinrnikhorsgni/dtsets 1/fundus fluore sein ngiogrm photogrphs of dieti ptients or http://misp.mui..ir/en/fundus%0fluoresent%0 Angiogrphy%0Imges. Sine, our proposed lgorithm for vessel segmenttion ws tested on the FFA imges from severl ptients in different types of dieti retinopthy in Isfhn Feiz hospitl; in order to ompre the proposed lgorithm with other methods, our vessel extrtion lgorithm ws tested on the green hnnel of DRIVE dtse to evlute the performne of proposed lgorithm. The experiments show tht the proposed lgorithm is more roust for deteting thin nd low ontrst vessels. The results of the proposed lgorithm for vessel segmenttion re shown in Figures 1 nd, for few smples of the fundus nd FFA imges of ove mentioned dtses, respetively. The proposed urvelet sed vessel extrtion lgorithm ording to left olumn of the funtionl lok digrm in Figure 1 is performed y MATLAB version 8 nd the omputtionl time is < min on 4.4 GHz proessor CORE i5. In this pper, FDCT vi wrpping is pplied in 5 sles nd 16 diretions is defined in the seond sles, whih mkes 3 diretions in 3 rd nd 4 th sles nd 64 diretions in 5 th sle to produe diretionl imges. Finlly, ontour f is deformed in eh of itertion using eqution f = f + f / t in order to move towrd oundry Figure 19: () Opti disk ndidte regions. () Atul lotion of opti disk Vol 5 Issue 3 Jul-Sep 015 Figure 0: () Originl imge. () Summtion of the imge resulting from Setion IVA nd the imge resulting from the modified fst disrete urvelet trnsform oeffiients, in Setion IVB. () Automted extrted opti disk region 151 Journl of Medil Signls & Sensors
Soltnipour, et l.: Automti nlysis of fundus fluoresein ngiogrm After otining finl vsulr tree segmenttion y itertively omining vessel enterlines imge with the set of imges resulting from the ggregted imges genertion phse, true positive rte (TPR), flse positive rte (FPR) nd ACC re used to evlute the results of the proposed lgorithm. TPR, FPR nd ACC re defined s follows: TP TPR = TP + FN FP FPR = FP + TN ( TP + TN) ury = TP + FP + TN + FN Figure 1: ( ) re relted to imges 01, 03 nd 39 from DRIVE dtset, respetively. From top to ottom, they re the originl imge, green hnnel, vessel enterlines, finl vessel segmenttion nd ground truth imge, respetively (15) (16) (17) Where, TP nd TN re the lood vessels pixels nd kground pixels whih orretly deteted, respetively, FP shows pixels whih do not elong to the vessels in the ground truth imge ut re deteted s the lood vessels, nd FN shows pixels whih elong to vessels in the ground proof imge ut re deteted s the kground. Tle 3 shows the performne of proposed method nd other previous pprohes in terms of TPR, FPR nd ACC for DRIVE dtse. Also, Tle 4 shows the performne of proposed lgorithm for vessel segmenttion in terms of TPR, FPR nd ACC for FFA imges from ngiogrphy unit of Isfhn Feiz Hospitl. Opti disk extrtion method Figure : ( ) Relted to fundus fluoresein ngiogrm imges from ngiogrphy unit of Isfhn Fiez Hospitl. From top to ottom, they re the originl imge, vessel enterlines, finl vessel segmenttion nd ground truth imge, respetively Tle 3: Comprison of performne etween our proposed method for vessel segmenttion nd others method on DRIVE dtse Method TPR FPR Averge ury A. M. Mendon et l. [3] 0.7344 0.036 0.9463 J. Stl et l. [] 0.6780 0.0170 0.9441 M. E. Mrtinez Perez et l. [3] 0.746 0.0345 0.9344 D. Mrin et l. [6] 0.7068 0.0305 0.945 Our method 0.7068 0.0068 0.943 TPR True positive rte; FPR Flse positive rte; FFA Fundus fluoresein ngiogrm Our proposed lgorithm for OD extrtion hs een evluted on the FFA imges of size 576 70 pixels from 15 Vol 5 Issue 3 Jul-Sep 015 Journl of Medil Signls & Sensors
Soltnipour, et l.: Automti nlysis of fundus fluoresein ngiogrm Tle 4: The performne mesure of vessel segmenttion lgorithm for FFA imges Method TPR FPR Averge ury Our lgorithm 0.736 0.051 0.9354 TPR True positive rte; FPR Flse positive rte; FFA Fundus fluoresein ngiogrm Tle 5: The performne mesure of OD extrtion lgorithm for FFA imges Method TPR R, R g1 R, R g FPR R, R g1 R, R g Our lgorithm 0.8815 0.000 0.8907 0.0001 Overlpping rtio etween R g1, R g Overlpping rtio etween R, R g1 Overlpping rtio etween R, R g 0.8563 0.8005 0.7903 R Deteted OD region utomtilly, nd R g1 nd R g re mnully leled OD regions y first nd seond experts respetively. TPR True positive rte; FFA Fundus fluoresein ngiogrm; OD Opti disk Figure 3: ( ) re relted to fundus fluoresein ngiogrm imges of ville dtset. From top to ottom, they re input imge, imge resulting from opti disk ontrst enhnement phse, opti disk oundry extrtion nd mnully leled opti disk region, respetively ville dtset. The proposed method for OD extrtion ording to right olumn of the funtionl lok digrm in Figure 1 is pplied y MATLAB version 8 nd the omputtionl time is less thn 1 minutes on 4.4 GHz proessor CORE i5. Figure 3 shows the results of the proposed method for few smples of the FFA imges. To evlute the results of OD extrtion, simple overlp mesure is used, tht is defined s follows: M nr ( R g) = nr ( R ) g Vol 5 Issue 3 Jul-Sep 015 (18) In Eq. (18), R nd R g stnd for the deteted OD region utomtilly nd mnully leled OD region y expert, respetively, n(r R g ) shows the numer of pixels in the intersetion of R nd R g nd n(r R g ) shows the numer of pixels in the union of R nd R g. Mnul detetion of the OD is delineted y two experts, independently. Tle 5 shows the verge performne of proposed method for OD extrtion on the defined dtset. In this tle, the men of the overlpping rtio for mnully deteted OD regions nd the men of the overlpping rtio etween OD regions deteted utomtilly nd mnully re lulted. Also, TPR nd FPR of the proposed method re omputed using (15) nd (16), where TP shows the numer of pixels tht elonging to OD nd re deteted y our proposed method orretly, TN shows the numer of pixels tht do not elonging to OD nd our proposed lgorithm is not deteted them s OD region, FP shows numer of pixels tht not elonging to OD ut our method is deteted them s OD region nd FN shows numer of pixels tht elong to OD ut our method does not detet them s OD region. The experimentl results show tht our proposed method for OD lotion nd oundry extrtion re suessful in oth norml nd norml imges. CONCLUSION AND FURTHER WORK In this pper, novel lgorithm in urvelet domin is introdued for min right ojets in the FFA imges. The proposed method strts with the extrtion of vessel enterlines. For this purpose, set of the diretionl imges is mde sed on the su nds urvelet trnsform. Due to high sensitivity of urvelet trnsform to the diretion in the different sles, urvelet trnsform is ple of mking diretionl imges suessfully. Then, eh diretionl imge is proessed in different sizes lood vessels dimeter sed on the informtion of the first order derivtive nd the eigenvlues of Hessin mtrix for extrting vessel enterlines. The finl segmenttion of the tree vessel is otined y omining vessel enterlines with set of imges resulting from the top ht trnsform followed y it plne sliing. Sine, the retinl lood vessels hve rnge of different sizes nd lso n our in different diretions, 153 Journl of Medil Signls & Sensors
Soltnipour, et l.: Automti nlysis of fundus fluoresein ngiogrm the nlysis of set of the diretionl imges in different sizes of lood vessels dimeter (nd their omintions) ws suitle for extrting thin nd smll vessels tht my e missed in finl segmenttion. Moreover, there is trde off etween extrting thin vessels nd removing the flse edges nd kground noise. Speifi hrteristis of FFA imges mke the OD oundry extrtion more diffiult ompring to olor fundus imges. Sine lood vessels nd OD re oth right strutures in the FFA imges; t first lood vessels re removed from retinl imge. Sine the right ojets in imges re ssigned to lrge urvelet oeffiients, FDCT is pplied on the imge resulting from vessel removl lgorithm to determine OD ndidte region. Regrding the struture of min r of the vessel round OD region, tul lotion of OD is extrted using evlution the Euliden distne etween the enters of OD ndidte regions nd onvergene point of the retinl vessels. Finlly, the oundry OD is extrted sed on the DRLSE model, whih hs unique FAB diffusion effet. So, the initil ontour is itertively onverged towrd OD oundry y more ACC. The experimentl results show tht the proposed method is vlid nd roust to extrt OD oundry in the FFA imges. In this pper we only disussed out the use of urvelet trnsform for urte detetion of vessels nd OD. The segmenttion of other importnt ojets in retinl imges suh s MAs, exudtes nd fove region n lso e done using the informtion of vessels nd OD. For exmple, simple mul detetion lgorithm n e used y onneting the vessels end points in predefined ROI sed on OD position. Or MAs nd exudtes ould e extrted fter removing right ojets from FFA imges nd using simple morphologil opertors. ACKNOWLEDGMENTS The uthors would like to thnk the ngiogrphy unit of Isfhn Feiz Hospitl for providing the FFA imges of norml nd norml retins, lso the Imge Sienes Institute, University Medil Center Utreht for mking the DRIVE dtset pulily ville. 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Employing the Hough trnsform to lote the opti disk. Biomed Si Instrum 001;37:81 6. 14. Niemeijer M, Aràmoff MD, vn Ginneken B. Segmenttion of the opti dis, mul nd vsulr rh in fundus photogrphs. IEEE Trns Med Imging 007;6:116 7. 15. Youssif AR, Ghlwsh AZ, Ghoneim AR. Opti dis detetion from normlized digitl fundus imges y mens of vessels diretion mthed filter. IEEE Trns Med Imging 008;7:11 8. 16. Esmeili M, Rni H, Dehnvi AM. Automti opti disk oundry extrtion y the use of urvelet trnsform nd deformle vritionl level set model, Pttern Reognition. Vol. 45, July, 01. p. 83 4. 17. Soltnipour A, Sdri S, Rni H, Akhlghi M, DoostHosseini A. Vessel Centerlines Extrtion from Fundus Fluoresein Angiogrm sed on Hessin Anlysis of Diretionl Curvelet Su nds. In Proeedings of the 38 th Interntionl Conferene on Aoustis, Speeh, nd Signl Proessing (ICASSP), Vnouver, Cnd; My 6 31, 013. 18. Li B, Sng N, Co Z. Contrst modulted nonliner diffusion for X ry ngiogrm imges. Conf Pro IEEE Eng Med Biol So 005;:1736 8. 19. Miri MS, Mhloojifr A. Retinl imge nlysis using urvelet trnsform nd multistruture elements morphology y reonstrution. IEEE Trns Biomed Eng 011;58:1183 9. 0. Cndes E, Demnet L, Donoho D, Ying L. Fst disrete urvelet trnsforms. Multisle Model Simul 006;5:861 99. 1. Moghimird E, Reztofighi SH, Soltnin Zdeh H. Multi sle pproh for retinl vessel segmenttion using medilness funtion. IEEE Int Symp Biomed Imging 010;1:9 3.. Vkilkndi MJ. Coronry Vessel Enhnement in Angiogrms Using Diretionl Filter Bnk, Ph.D. Disserttion, Deprtment of Eletril Engineering Isfhn University, Isfhn 84156 83111, Irn; 011. 3. Mendonç AM, Cmpilho A. Segmenttion of retinl lood vessels y omining the detetion of enterlines nd morphologil reonstrution. IEEE Trns Med Imging 006;5:100 13. 4. Li C, Xu C, Gui C, Fox MD. Distne regulrized level set evolution nd its pplition to imge segmenttion. IEEE Trns Imge Proess 010;19:343 54. How to ite this rtile: Soltnipour A, Sdri S, Rni H, Akhlghi MR. Anlysis of Fundus Fluoresein Angiogrm Bsed on the Hessin Mtrix of Diretionl Curvelet Su nds nd Distne Regulrized Level Set Evolution. J Med Sign Sene 015;5:141-55. Soure of Support: Nil, Conflit of Interest: None delred 154 Vol 5 Issue 3 Jul-Sep 015 Journl of Medil Signls & Sensors
Soltnipour, et l.: Automti nlysis of fundus fluoresein ngiogrm BIOGRAPHIES Asieh Soltnipour reeived her Ms in Eletril Engineering from Isfhn University of Tehnology in 013. Her min reserh re re signl, imge nd video proessing. E-mil: sieh.soltnipour@yhoo.om Seed Sdri is n Assoite Professor in Eletril nd Computers Engineering Deprtment, Isfhn University of tehnology, Isfhn, Irn. He reeived the B.S. nd the M.S. degree in eletril engineering from the University of Tehrn, Fulty of Engineering, Tehrn, Irn, respetively in 1977 nd 1979. He reeived the PhD degree in ommunition engineering from the Isfhn University of Tehnology, in 1997. E-mil: sdri@.iut..ir Hossein Rni reeived the B.S. degree in Eletril Engineering (Communitions) from Isfhn University of Tehnology, Isfhn, Irn, in 000 with the highest honors, nd the M.S. nd Ph.D. degrees in Bioeletril Engineering in 00 nd 008, respetively, from Amirkir University of Tehnology (Tehrn Polytehni), Tehrn, Irn. In 007 he ws with the Deprtment of Eletril nd Computer Engineering, Queen s University, Kingston, ON, Cnd, s Visiting Reserher, in 011 with the University of Iow, IA, United Sttes, s Postdotorl Reserh Sholr, nd in 013-014 with Duke University Eye Center s Postdotorl Fellow. He is now n Assoite Professor in Biomedil Engineering Deprtment nd Medil Imge & Signl Proessing Reserh Center, Isfhn University of Medil Sienes, Isfhn, Irn. Dr. Rni is Senior Memer of IEEE (Signl Proessing Soiety, Engineering in Mediine nd Biology Soiety, Ciruits nd Systems Soiety, Computer Soiety), nd Editorin-Chief of Journl of Medil Signls nd Sensors. His min reserh interests re medil imge nlysis nd modeling, sttistil (multidimensionl) signl proessing, sprse trnsforms, nd imge restortion, whih more thn 110 ppers nd ook hpters hve een pulished y him s n uthor or o-uthor in these res. E-mil: h_rni@med.mui..ir E-mil: khlghi@med.mui..ir Mohmmd Rez Akhlghi is n Assoite Professor in Deprtment of Ophthlmology, Isfhn University of Medil Sienes, Isfhn, Irn. He reeived his VitreoRetinl Fellowship in 006 from the Tehrn University of Medil Sienes. Quik Response Code link for full text rtiles The journl issue hs unique new feture for rehing to the journl s wesite without typing single letter. Eh rtile on its first pge hs Quik Response Code. Using ny moile or other hnd-held devie with mer nd GPRS/other internet soure, one n reh to the full text of tht prtiulr rtile on the journl s wesite. Strt QR-ode reding softwre (see list of free pplitions from http://tinyurl.om/ yzlht) nd point the mer to the QR-ode printed in the journl. It will utomtilly tke you to the HTML full text of tht rtile. One n lso use desktop or lptop with we mer for similr funtionlity. See http://tinyurl.om/w7fn3 or http://tinyurl.om/3ysr3me for the free pplitions. Vol 5 Issue 3 Jul-Sep 015 155 Journl of Medil Signls & Sensors