Optic disc (OD) detection is an important step in a number of algorithms developed for automatic extraction of anatomical structures and retinal lesions. In this article, a novel system, eye gaze– based OD detection, is presented for detecting OD in fundus retinal image using the knowledge developed from the expert’s eye gaze data. The eye gaze data are collected from expert optometrists and non-experts group while viewing the fundus retinal images. The task given to them is to spot the OD in fundus retinal images. Eye gaze fixations were used to identify the target and distractor regions. The image-based features were extracted from the identified regions. The top-down (TD) knowledge is developed using feature ranking and fuzzy system. This TD knowledge is further used for building the TD map. The success rates for various standard datasets are: DRIVE dataset, 100%; DRIONS-DB, 98.2%; INSPIRE, 97.5%; High Resolution Fundus Images, 100%; DIRECTDB0, 96.9%; ONHSD, 91.9% and STARE, 81.4%.
KulkarniN. and AmudhaJ., A study on current optic disc detection methods, International Journal of Control Theory and Applications9(40) (2016), 441–452.
2.
HaggstromM., Fundus photograph of normal left eye.jpg, WikiJournal of Medicine (2012).
3.
WisaengK., HiransakolwongN. and PothirukE., Automatic detection of optic disc in digital retinal images, International Journal of Computer Applications90(5) (2014), 15–20.
4.
GodseD.A. and BormaneD., Automated localization of optic disc in retinal images, International Journal of Advanced Computer Science and Applications4(2) (2013).
5.
MendonçaA., SousaA., MendonçaL. and CampilhoA., Automatic localization of the optic disc by combining vascular and intensity information, Computerized Medical Imaging and Graphics37(5-6) (2013).
6.
LuS. and LimJ., Automatic optic disc detection from retinal images by a line operator, IEEE Transactions on Biomedical Engineering58(1) (2011).
7.
KulkarniN. and AmudhaJ., Relevance of computational model for detection of optic disc in retinal images, International Journal of Computer Technology & Applications5(1896) (2014).
8.
KhalidN., NoorN.M. and AriffaN., Fuzzy c-Means (FCM) for optic cup and disc segmentation with morphological operation, International Conference on Robot PRIDE42 (2014).
9.
MuntasaA., SiradjuddinI.A. and SophanM., Hybrid method based retinal optic disc detection, International Journal of New Computer Architectures and their Applications102 (2015).
10.
MithunN., DasS. and FattahS., Automated detection of optic disc and blood vessel in retinal image using morphological, edge detection and feature extraction techniques, 16th International Conference on Computer and Information TechnologyKhulna, Bangladesh (2014).
11.
DehghaniA., MoghaddamH.A. and MoinM.-S., Optic disc localization in retinal images using histogram matching, EURASIP Journal on Image and Video Processing2012(19) (2012).
12.
FragaA., BarreiraN., OrtegaM., PenedoM.G. and CarreiraM.J., Precise segmentation of the optic disc in retinal fundus images, Computer Aided SystemsTheory{—EUROCAST 2011 pp. 584–591, Springer (2012).
13.
TjandrasaH., WijayantiA. and SuciatiN., Optic nerve head segmentation using hough transform and active contours, Telkomnika10(3) (2012), 531–536.
StaalJ.J., AbramoffM.D., NiemeijerM., ViergeverM.A. and GinnekenB., Ridge based vessel segmentation in color images of the retina, IEEE Transactions on Medical Imaging23 (2004), 501–509.
16.
HooverA., KouznetsovaV. and GoldbaumM., Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response, IEEE Transactions on Medical Imaging19(203) (2000).
17.
KulkarniN. and AmudhaJ., Comparison of experts and non-experts gaze pattern for the target as optic disc in the fundus retinal images, International Journal of Applied Engineering Research12(14) (2017), 4106–4112.
18.
KulkarniN. and AmudhaJ., An eye gaze based approach for labeling regions in fundus retinal images, Accepted as Poster in 19th European Conference on Eye Movements, ECEM, (2017).
19.
IttiL. and KochC., Computational modeling of visual attention, Nature Reviews Neuroscience2(3) (2001), 194–203.
20.
IttiL., KochC. and NieburE., A model of saliency-based visual attention for rapid scene analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence20(11) (1998), 1254–1259.
21.
FrintropS., KlodtM. and RomeE., A real time visual attention system using integral images, In:International Conference on Computer Vision Systems (ICVS 2007), (2007).
22.
KochC. and UllmanS., Shifts in selective visual attention: Towards the underlying neural circuitry, Human Neurobiology4(4) (1985), 219–227.
23.
TreismanA. and GeladeG., A feature-integration theory of attention, Cognitive Psychology12(1) (1980), 97136.
24.
WolfeJ.M., Guided search 2.0: A revised model of visual search, Psychonomic Bulletin and Review1(2) (1994), 202–238.
25.
PalmerS.E., Vision science: Photons to phenomenology . Cambridge: MIT Press, (2002).
26.
GreenspanH., BelongieS., GoodmanR., PeronaP., RakshitS. and AndersonC.H., Overcomplete steerable pyramid filters and rotations invariance, Proc IEEE Computer Vision and Pattern Recognition (CVPR), Seattle, Washington, pp. 222–228 (1994).
27.
KulkarniN. and AmudhaJ., Feature extraction and selection for classification of attractor’s and distractor’s region in fundus retinal images, Proceedings of International Conference on Recent Innovations in Engineering and Technologies (ICRIET-2K16) (2016), ISBN: 978-93-86163-56-1.
28.
NiemeijerM., XuX., DumitrescuA., GuptaP. and van GinnekenB., FolkJ. and AbramoffM., Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs, IEEE Transactions on Medical Imaging30(11) (2011), 1941–1950.
29.
KöhlerT., BudaiA., KrausM., OdstrcilikJ., MichelsonG. and HorneggerJ., Automatic no-reference quality assessment for retinal fundus images using vessel segmentation, 26th IEEE Internatioal Symposium on Computer-Based Medical Systems, pp. 95–100, IEEE (2013).
30.
KauppiT., KalesnykieneV., LensuK.J., SorriL., UusitaloI., KalviainenH. and PietilaJ., DIARETDB0, Machine Vision and PR Research Group, Finland, (2006).
31.
LalondeM., BeaulieuM. and GagnonL., Fast and robust optic disc detection using pyramidal decomposition and hausdorff based template matching, IEEE Transactions on Medical Imaging20(11) (2001), 1193–1200.
32.
RoychowdhuryS., KoozekananiD.D., KuchinkaS.N. and ParhiK.K., Optic disc boundary and vessel origin segmentation of fundus images, IEEE Journal of Biomedical and Health Informatics20(6) (2016), 1562–1574.
33.
CarmonaE.J., RincónM., García-FeijooJ. and Martínez-de-la-CasaJ.M., Identification of the optic nerve head with genetic algorithms, Artificial Intelligence in Medicine43(3) (2008), 243–259.
34.
LowellJ., HunterA., SteelD., RyderB. and FletcherE., Optic nerve head segmentation, IEEE Transactions on Medical Imaging23(2) (2004).
35.
AggarwalM. and KhareV., Automatic localization and contour detection of optic disc, International Conference on Signal Processing and Communication, Noida, India (2015).
36.
WelferD., ScharcanskiJ. and MarinhoD.R., A morphologic two-stage approach for automated optic disk detection in color eye fundus images, Pattern Recognition Letters34(5) (2012), 476–485.
37.
WangC., KabaD. and LiY., Level set segmentation of optic discs from retinal images, Journal of Medical and Bioengineering4(3) (2015).
38.
DevasiaT., JacobP. and ThomasT., Automatic extraction and localisation of optic disc in colour fundus images, IJCSI International Journal of Computer Science Issues12(3) (2014).
39.
RezaM.N. and AhmadM., Automatic detection of optic disc in fundus images by curve operator, Proceedings of International Conference on Electrical Information and Communication Technology, EICT, (2015).
40.
PradhepaK., KarkuzhaliS. and ManimegalaiD., Segmentation and localization of optic disc using feature match and medial axis detection in retinal images, Biomedical & Pharmacology Journal8(1) (2015), 391–397.
41.
ClaroM., SantosL., SilvaW., AraujoF. and MouraN., Automatic glaucoma detection based on optic disc segmentation and texture feature extraction, CLEI Electronic Journal19(2) (2016).
42.
SopharakA., UyyanonvaraB., BarmanS. and WilliamsonT.H., Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods, Computerized Medical Imaging32(8) (2008), 720–727.
43.
KandeG.B., SubbaiahP.V. and SatyaS.T., Segmentation of exudates and optic disk in retinal images, Proceedings of Sixth Indian Conference on Computer Vision, Graphics & Image Processing 16-19 Dec. 2008, Bhubaneswar, India, (2008).
44.
LupascuC.A., TegoloD. and RosaL.D., Automated detection of optic disc location in retinal images, in Proceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems (CBMS ’08) pp. 17–22, IEEE, Jyvaskyla, Finland, June (2008).
45.
GirardF., KavalecC., GrenierS., TaharH.B. and CherietF., Simultaneous macula detection and optic disc boundary segmentation in retinal fundus images, SPIE 9784, Medical Imaging 2016: Image Processing 97841F 21 March (2016).
46.
DashtbozorgB. and MendonçaA.M., and CampilhoA., Optic disc segmentation using the sliding band filter, Computers in Biology and Medicine56(2015) (2015), 1–12.
