Diagnosis of ophthalmologic disordersin retinal fundus images

Automated fundus image analysis plays an important role in the computer aided diagnosis of ophthalmologic disorders. A lot of eye disorders, as well as cardiovascular disorders, are known to be related with retinal vasculature changes. Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this paper focuses on retinal image analysis and their clinical implications. The most prevalent causes of blindness in the industrialized world are age-related macular degeneration, diabetic retinopathy, and glaucoma. Retinal exudates are among the preliminary signs of diabetic retinopathy, a major cause of vision loss in diabetic patients. Correct and efficient screening of exudates is very expensive in professional time and may cause human error. Nowadays, the digital retinal image is frequently used to follow-up and diagnoses eye diseases. Therefore, the retinal image is crucial and essential for experts to detect exudates. In age related Macular degeneration, the macula is responsible for the sharp central vision needed for detailed activities such as reading, writing, driving, face recognition and ability to see colors. Age related macular degeneration is degeneration of the macula area and the delicate cells of the macula become inactive and stop working. Unfortunately, age-related macular degeneration cannot be completely cured, but if diagnosed at an early stage degeneration laser treatment can help some people to prevent further deterioration of macula. The algorithm locates disease affected pixels on macula and displays their location. After pre-processing particle analysis tool is applied to locate the effected parts on the fundus image.

[1]  Cemal Köse,et al.  Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images , 2012, Comput. Methods Programs Biomed..

[2]  Uğur Şevik,et al.  Automatic segmentation of age-related macular degeneration in retinal fundus images , 2008, Comput. Biol. Medicine.

[3]  Philippe Burlina,et al.  Automatic screening of age-related macular degeneration and retinal abnormalities , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[5]  Michalis E. Zervakis,et al.  Detection and segmentation of drusen deposits on human retina: Potential in the diagnosis of age-related macular degeneration , 2003, Medical Image Anal..

[6]  H. S. Bhadauria H.S. Bhadauria Vessels Extraction from Retinal Images , 2013 .

[7]  J. Olson,et al.  Automated detection of microaneurysms in digital red‐free photographs: a diabetic retinopathy screening tool , 2000, Diabetic medicine : a journal of the British Diabetic Association.

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

[9]  Jaspreet Kaur,et al.  An Efficient Blood Vessel Detection Algorithm For Retinal Images Using Local Entropy Thresholding , 2012 .

[10]  Bahadir Karasulu AUTOMATIC EXTRACTION OF RETINAL BLOOD VESSELS: A SOFTWARE IMPLEMENTATION , 2012 .