Exudate segmentation on retinal atlas space

Diabetic macular edema is characterized by hard exudates. Presence of such exudates cause vision loss in the affected areas. We present a novel approach of segmenting exudates for screening and follow-ups by building an ethnicity based statistical atlas. The chromatic distribution in such an atlas gives a good measure of probability of the pixels belonging to the healthy retinal pigments or to the abnormalities (like lesions, imaging artifacts etc.) in the retinal fundus image. Post-processing schemes are introduced in this paper for the enhancement of the edges of such exudates for final segmentation and to separate lesion from false positives. A sensitivity(recall) of 82.5 % at 35% of positive predictive value on FROC-curve is achieved. Results are obtained on a publicly available HEI-MED dataset and have been compared to two reference methods on the same dataset showing the competitiveness of the proposed algorithm.

[1]  Roberto Hornero,et al.  Neural network based detection of hard exudates in retinal images , 2009, Comput. Methods Programs Biomed..

[2]  Hong Shen,et al.  Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms , 1999, IEEE Transactions on Information Technology in Biomedicine.

[3]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

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

[5]  Dimitri Van De Ville,et al.  Wavelet Steerability and the Higher-Order , 2010 .

[6]  Roberto Hornero,et al.  Retinal image analysis based on mixture models to detect hard exudates , 2009, Medical Image Anal..

[7]  Dimitri Van De Ville,et al.  Lung Texture Classification Using Locally-Oriented Riesz Components , 2011, MICCAI.

[8]  Sharib Ali,et al.  Steerable wavelet transform for atlas based retinal lesion segmentation , 2013, Medical Imaging.

[9]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[10]  Kenneth W. Tobin,et al.  Exudate-based diabetic macular edema detection in fundus images using publicly available datasets , 2012, Medical Image Anal..

[11]  Joseph M. Reinhardt,et al.  Retinal atlas statistics from color fundus images , 2010, Medical Imaging.

[12]  R A Kirsch,et al.  Computer determination of the constituent structure of biological images. , 1971, Computers and biomedical research, an international journal.

[13]  M. J. Carreira,et al.  Localization and Extraction of the Optic Disc Using the Fuzzy Circular Hough Transform , 2006, ICAISC.

[14]  E. Chaum,et al.  A health insurance portability and accountability act-compliant ocular telehealth network for the remote diagnosis and management of diabetic retinopathy. , 2011, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[15]  Kenneth W. Tobin,et al.  Automatic retina exudates segmentation without a manually labelled training set , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[16]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[17]  B. van Ginneken,et al.  Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. , 2007, Investigative ophthalmology & visual science.

[18]  R. Bourne,et al.  Ethnicity and ocular imaging , 2011, Eye.

[19]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

[20]  P. Sharp,et al.  Automated detection and quantification of retinal exudates , 1993, Graefe's Archive for Clinical and Experimental Ophthalmology.

[21]  Bunyarit Uyyanonvara,et al.  Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering , 2009, Sensors.

[22]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[23]  Enrico Grisan,et al.  Luminosity and contrast normalization in retinal images , 2005, Medical Image Anal..