You are here

Home » Content

GÖRSEL SÖZLÜK VE SINIFLANDIRMA YAKLAŞIMLARINDAN FAYDALANARAK DİYABETİK RETİNOPATİLİ RETİNAL GÖRÜNTÜLERDE SERT EKSUDALARIN TESPİTİ

DETECTION OF HARD EXUDATES IN DIABETIC RETINOPATHY RETINAL IMAGES BY UTILIZING VISUAL DICTIONARY AND CLASSIFIER APPROACHES

Journal Name:

  • Mugla Journal of Science and Technology

Publication Year:

  • 2016

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
Diabetic retinopathy is a disease that causes blindness resulting from damages that emerge in the retina depending on the diabetes mellitus. There are two stages of the disease including the non-proliferative and proliferative. Eyesight loss is blocked by means of early detection and diagnosis of non-proliferative DR findings. In this study, we designed a decision support system for automatic detection of hard exudates which are early stage DR lesions. This system consists of keypoint extraction, feature extraction, visual dictionary and classifying stages. We tested the performance of the system, which we carried out based on system learning and analysis of new retinal images, on the public DIARETDB1 retinal image dataset. Experimental results obtained with Artificial Neural Networks, Random Forest ve Decision Tree algorithms showed us that machine learning technique suggested by us is successful.
Bookmark/Search this post with
Abstract (Original Language): 
Diyabetik retinopati, şeker hastalığına bağlı olarak retinada ortaya çıkan hasarlanmaların sonucu körlüğe neden olan bir hastalıktır. Bu hastalığın erken evre (nonproliferatif) ve ileri evre (proliferative) olmak üzere iki aşaması vardır. Erken evre DR bulgularının erken tanı ve teşhisi sayesinde görme kaybının önüne geçilir. Bu çalışmamızda erken evre DR lezyonlarından olan sert eksuda bölgelerinin otomatik olarak tespiti için bir karar destek sistemi tasarladık. Bu sistem, anahtar nokta çıkarımı, özellik çıkarımı, görsel sözlük ve sınıflandırma aşamalarını içerir. Sistemin öğrenmesi ve yeni retinal görüntülerin analizi temeline dayanarak gerçekleştirdiğimiz bu sistemin performansını publik (herkese açık) DIARETDB1 retinal görüntü dataseti üzerinde test ettik. Yapay Sinir Ağları, Rastgele Orman ve Karar Ağacı algoritmaları ile elde ettiğimiz deneysel sonuçlar önerdiğimiz makina öğrenmesi tekniğinin başarılı olduğunu bize göstermiştir.
FULL TEXT (PDF): 
PDF icon arastirmax-gorsel-sozluk-siniflandirma-yaklasimlarindan-faydalanarak-diyabetik-retinopatili-retinal-goruntulerde-sert-eksudalarin-tespiti.pdf
  • 1
1
6
  • Turkish

REFERENCES

References: 

1] Mohamed, Q., Gillies, M.C., Wong, T.Y., “Management of diabetic retinopathy: A systematic review”, Jama-J Am Med Assoc, 298(8), 902-916, 2007.
[2] Venkatesan, R., Chandakkar, P., Li, B., Li, H.K., “Classification of diabetic retinopathy images using multi-class multiple-instance learning based on color
correlogram features”, Conf Proc IEEE Eng Med Biol Soc, 1462-1465, San Diego, 2012.
[3] Kauppi, T., Kalesnykiene, V., Kamarainen, J.K., Lensu, L., Sorri, I., Raninen, A., Voutilainen, R., Pietila, J., Kalviainen, H., Uusitalo, H., “Diaretdb1 diabetic retinopathy database and evaluation protocol”, Proceedings of the Medical Image Understanding and Analysis, 2007.
[4] Chen, X., Bu, W., Wu, X., Dai, B., Teng, Y., “A novel method for automatic hard exudates detection in color retinal images”, in IEEE International Conference on Machine Learning and Cybernetics, 1175-1181, Xian, 2012.
[5] Garcia, M., Valverde, C., Lopez, M.I., Poza, J., Hornero, R., “Comparison of logistic regression and neural network classifiers in the detection of hard exudates in retinal images”, in IEEE International Conference on Engineering in Medicine and Biology Society, 5891-5894, Osaka, 2013.
[6] Hsu, W., Pallawala, P.M.D., Mong L.L., Kah-Guan A.E., “The role of domain knowledge in the detection of retinal hard exudates”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, , 2, 246-251, 2001.
[7] Kayal, D., Banerjee, S., “A new dynamic thresholding based technique for detection of hard exudates in digital retinal fundus image”, International Conference on Signal Processing and Integrated Networks (SPIN), Noida, 2014, 141-144, 2014.
[8] Mishra, A.M., Singh, P.K., Chawla, K.S., “An information geometry based scheme for hard exudate detection in fundus images”, India Conference (INDICON), 1-4, Hyderabad, 2011.
[9] Ranamuka, N.G., Meegama, R.G.N., “Detection of hard exudates from diabetic retinopathy images using fuzzy logic”, IET Image Process, 7(2), 121-130, 2013.
Kemal AKYOL, Şafak BAYIR, Baha ŞEN
Mugla Journal of Science and Technology, Vol 2, No 1, 2016, Pages 1-6
6
[10] Eadgahi, M.G.F., Pourreza, H., “Localization of hard exudates in retinal fundus image by mathematical morphology operations”, 2nd International Conference on Computer and Knowledge Engineering (ICCKE), 185-189, Mashhad, 2012.
[11] Sanchez, C.I., Niemeijer, M., Suttorp Schulten, M.S.A., Abramoff, M., Van Ginneken, B., “Improving hard exudate detection in retinal images through a combination of local and contextual information”, International Symposium on Biomedical Imaging: From Nano to Macro, 5-8, Rotterdam, 2010.
[12] Sanchez, C.I., Mayo, A., Garcia, M., Lopez, M.I., Hornero, R., “Automatic image processing algorithm to detect hard exudates based on Mixture Models”, 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 4453 – 4456, New York, 2006.
[13] Tjandrasa, H., Putra, R.E., Wijaya, A.Y., Arieshanti, I., “Classification of non-proliferative diabetic retinopathy based on hard exudates using soft margin SVM”, in IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 376-380, Mindeb, 2013.
[14] Xu, L., Luo, S., “Support vector machine based method for identifying hard exudates in retinal images”, in IEEE Youth Conference on Information Computing and Telecommunication, 138-141, Beijing, 2009.
[15] Sanchez, C.I., Hornero R., Lopez, M.I., Aboy, M., Poza, J., Abasolo, D., “A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis”, Med Eng Phys, 30(3), 350–357, 2008.
[16] Pereira, C., Gonçalves, L., Ferreira, M., “Exudate segmentation in fundus images using an ant colony optimization approach”, Inform Sciences, 296, 14-24, 2015.
[17] Garcia M., Sanchez C.I., Lopez M.I., Abasolo D., Hornero R., “Neural network based detection of hard exudates in retinal images”, Comput Meth Prog Bio, 93(1), 9-19, 2009.
[18] JayaKumari, C., Maruthi, R., “Detection of hard exudates in color fundus images of the human retina”, Procedia Engineering, 30, 297-302, 2012.
[19] Stark, J.A., “Adaptive image contarst enhancement using generalizations of histogram equalization” IEEE T Image Process, 9(5), 889-894, 2000.
[20] Lowe, D.G., “Distinctive image features from scale-invariant keypoints”, Int J Comput Vision, 60(2), 91-110, 2004.
[21] Bay, H., Ess, A., Tuytelaars, T., Gool, L.V., “Speeded-Up Robust Features (SURF)”, Comput Vis Image Und, 110(3), 346-359, 2008.
[22] Rublee, E., Rabaud, V., Konolige, K., Bradski, G., “ORB: an efficient alternative to SIFT or SURF”, IEEE I Conf Comp Vis, 2564-2571, Barcelona, 2011.
[23] Ozuysal, M., Calonder, M., Lepetit, V., Fua, P., “Fast keypoint recognition using random ferns”, IEEE T Pattern Anal, 32(3), 448-461, 2010.
[24] Hwang, S.K., Billinghurst, M., Whoi-Yul, K., “Local descriptor by zernike moments for real-time keypoint matching”, Lect Notes Comput Sc, 2, 781-785, Sanya, 2008.
[25] Hu, X., Tang, Y., Zhang, Z., “Video object matching based on SIFT algorithm”, in IEEE International Conference on Neural Networks and Signal Processing, 412- 415, Nanjing, 2008.
[26] Kuo H.Y., Su H.R., Lai, S.H., Wu, C.C., “3D object detection and pose estimation from depth image for robotic bin picking”, in IEEE International Conference on Automation Science and Engineering (CASE), 1264-1269, Taipei, 2014.
[27] Wang, Z., Xiao, H., He, W., Wen, F., Yuan, K. “Real-time SIFT-based object recognition system”, in IEEE International Conference on Mechatronics and Automation (ICMA), 1361-1366, Takamatsu, 2013.
[28] Tola, E., Lepetit, V., Fua, P., “DAISY: An efficient dense descriptor applied to wide-baseline Stereo”, IEEE T Pattern Anal, 32(5), 815-830, 2009.
[29] Chao, Z., Bichot, C.E., Liming, C., “Visual object recognition using DAISY descriptor”, in IEEE International Conference on Multimedia and Expo (ICME), 1-6, Barcelona, 2011.
[30] Guo, Y., Mu, Z.C., Zeng, H., Wang, K., “Fast rotation-invariant DAISY descriptor for image keypoint matching”, in IEEE International Symposium on Multimedia (ISM), 183-190, Taichung, 2010.
[31] Umesh, K.K., Suresha, “Web image retrieval using visual dictionary”, International Journal on Web Service Computing (IJWSC), 3(3), 77-84, 2012.
[32] Jain, L.C., Martin, N.M., “Fusion of neural networks, fuzzy sets, and genetic algorithms: industrial applications”, CRC press, 1998.
[33] Aggarwal, C.C., “Data classification algorithms and applications”, In: Kumar V, editor. Data Mining and Knowledge Discovery Series, CRC press, 11-16, 493-495, 2014.
[34] Breiman, L., “Random forests”, Mach Learn, 45, 5-32, 2001.
[35] Akar, Ö., Güngör, O., “Classification of multispectral images using Random Forest algorithm”, Journal of Geodesy and Geoinformation, 1(2), 105-112, 2012.
[36] Zhu, W., Zeng, N., Wang, N., “Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations”, In: NESUG proceedings: Health Care and Life Sciences, Baltimore, 2010.
[37] Wong, H.B., Lim, G.H., “Measures of diagnostic accuracy: sensitivity, specificity, PPV and NPV”, Proceedings of Singapore healthcare, 20, 316-318, 2011.

Thank you for copying data from http://www.arastirmax.com