Analysis of Fundus Image of Ophthalmoscope for Macula Identification and Detection to Diagnosis of Vision Related Diseases using Graythresholding and Pixel Index Number

As we know the diabetic causes people are increasing day by day in the world. Diabetic disease has also infects the other parts of the human body, like eye, platelets counts etc. In the field of medicines, medical image processing plays a vital role to detect the abnormalities of eye or eye diseases like glaucoma and diabetic retinopathy. Macular degeneration is one of the medical conditions that affect the vision of elder people [1]. The retina is responsible for peripheral vision. Macula is the central area of the retina, temporal to the optic disk. It is responsible to have fine central vision and colour vision. This paper provides the Manual analysis of the images can be improved and problem of detection diabetics by identifying and detecting macula using gray thresholding and index number of object. General Terms Ophthalmology engines

[1]  Zhiheng Xu,et al.  Tracking retinal motion with a scanning laser ophthalmoscope. , 2005, Journal of rehabilitation research and development.

[2]  P. Zimmet,et al.  The Rising Global Burden of Diabetes and its Complications: Estimates and Projections to the Year 2010 , 1997, Diabetic medicine : a journal of the British Diabetic Association.

[3]  Tien Yin Wong,et al.  Automatic detection of the macula in retinal fundus images using seeded mode tracking approach , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  N.M. Tan,et al.  Automatic detection of the macula in the retinal fundus image by detecting regions with low pixel intensity , 2009, 2009 International Conference on Biomedical and Pharmaceutical Engineering.

[5]  K. Chan,et al.  Towards automatic detection of age-related macular degeneration in retinal fundus images , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[6]  Shijian Lu,et al.  Automatic macula detection from retinal images by a line operator , 2010, 2010 IEEE International Conference on Image Processing.

[7]  P. Jong Prevalence of age-related macular degeneration in the United States. , 2004 .

[8]  Kenneth W. Tobin,et al.  Detection of Anatomic Structures in Human Retinal Imagery , 2007, IEEE Transactions on Medical Imaging.

[9]  Benita J. O’Colmain,et al.  Prevalence of age-related macular degeneration in the United States. , 2004, Archives of ophthalmology.

[10]  J. Liu,et al.  Optic cup and disk extraction from retinal fundus images for determination of cup-to-disc ratio , 2008, 2008 3rd IEEE Conference on Industrial Electronics and Applications.

[11]  Romuere Rôdrigues Veloso e Silva,et al.  Assessing the accuracy of macula detection methods in retinal images , 2013, 2013 18th International Conference on Digital Signal Processing (DSP).