Comparative analysis of automatic exudate detection with traditional and machine learning methods

To prevent and reduce the number of blindness in diabetic patients, periodic screening, automated early exudate detection, and early diagnosis are neccessary. Traditional automatic exudates detections requires many predefined parameters or features while machine learning methods learns and adjusts those parameter automatically but they need time to train. We implemente and investigate benefit of both approaches and a comparative analysis of traditional and machine learning of exudates detections, namely, mathematical morphology, fuzzy c-means clustering, naive Bayesian classifier, Support Vector Machine and Nearest Neighbour classifier is presented. Detected exudates are validated with expert ophthalmologists’ hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared.

[1]  T. Williamson,et al.  Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. , 1996, The British journal of ophthalmology.

[2]  Ole Vilhelm Larsen,et al.  Screening for diabetic retinopathy using computer based image analysis and statistical classification , 2000, Comput. Methods Programs Biomed..

[3]  Abdesselam Bouzerdoum,et al.  Skin segmentation using color pixel classification: analysis and comparison , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Bunyarit Uyyanonvara,et al.  Automatic Exudates Detection on Thai Diabetic Retinopathy Patients' Retinal Images , 2006 .

[5]  R. Hornero,et al.  Retinal image analysis to detect and quantify lesions associated with diabetic retinopathy , 2003, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Bunyarit Uyyanonvara,et al.  Automatic exudates detection from diabetic retinopathy retinal image using fuzzy C-means and morphological methods , 2007 .

[7]  AKARA SOPHARAK,et al.  AUTOMATIC EXUDATES DETECTION FROM NON-DILATED DIABETIC RETINOPATHY RETINAL IMAGE USING FUZZY C-MEANS CLUSTERING , 2007 .

[8]  Xiaohui Zhang,et al.  Top-down and bottom-up strategies in lesion detection of background diabetic retinopathy , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Majid Mirmehdi,et al.  Comparative Exudate Classification Using Support Vector Machines and Neural Networks , 2002, MICCAI.

[10]  C. Sinthanayothin,et al.  Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images , 1999, The British journal of ophthalmology.

[11]  Bunyarit Uyyanonvara,et al.  Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods , 2008, Comput. Medical Imaging Graph..

[12]  B. Thomas,et al.  Automated identification of diabetic retinal exudates in digital colour images , 2003, The British journal of ophthalmology.

[13]  J. Boyce,et al.  Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening , 2004, Diabetic medicine : a journal of the British Diabetic Association.

[14]  Yin Aye Moe,et al.  Automatic Exudate Detection with a Naive Bayes Classifier , 2008 .

[15]  Pascale Massin,et al.  A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina , 2002, IEEE Transactions on Medical Imaging.

[16]  Bunyarit Uyyanonvara,et al.  Automatic exudate detection with a support vector machine classifier , 2008 .

[17]  Majid Mirmehdi,et al.  Automatic Recognition of Exudative Maculopathy using Fuzzy C- Means Clustering and Neural Networks , 2001 .

[18]  C. Sinthanayothin,et al.  Automated detection of diabetic retinopathy on digital fundus images , 2002, Diabetic medicine : a journal of the British Diabetic Association.

[19]  Shankar M. Krishnan,et al.  Automatic image analysis of fundus photograph , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[20]  Ian H. Witten,et al.  Data Mining: Practical Machine Learning Tools and Techniques, 3/E , 2014 .