By Amit Kumar, Fahimuddin Shaik
This ebook is a suite of all of the experimental effects and research performed on scientific photographs of diabetic similar motives. The experimental investigations were performed on pictures ranging from very simple photo processing thoughts similar to snapshot enhancement to stylish picture segmentation equipment. This ebook is meant to create an understanding on diabetes and its similar reasons and photo processing equipment used to become aware of and forecast in an easy method. This booklet comes in handy to researchers, Engineers, doctors and Bioinformatics researchers.
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13. So prewitt edge detection doesn’t give the clear edges for identifying the problem present in that image. 14. This method doesn’t give the clear edges for identifying the problem present in that image. 15. By observing this figure it is clear that there is less number of discontinuities are present. Log edge detection method gives the somewhat clear edges for identifying the problem present in that image. 16. By observing this figure it is clear that there is less number of discontinuities compare to other edge detection methods (Sobel, Prewitt, Robert, LoG).
So, the patient can be educated to find maculopathy at an early stage and hence the loss of vision can be prevented. In future lot development has to be done on this algorithms such that the Endocrinologists and Opthamalogists may employ this algorithm for earlier detection of Diabetic Retinopathy for easy analysis and to study the pathologies. The final part of the work is about the detection of Diabetic Myonecrosis is a rare and often-missed disease that occurs in patients with poorly controlled diabetes which is analyzed visually using MRI image and its FCM segmented image.
In majority of the CSME images, the R3 and R4 values are higher as compare to non-CSME images due to the area tends to be white. Thus the number of white pixels is more in this class. In normal image, the values are zero due to black area on the parafovea and perifovea regions. 3 Results and Test Performance A data base of 90 samples is used for training and testing the classifiers. These samples are divided into 90 % training and 10 % for testing. The performance measures such as sensitivity, specificity and positive predictive accuracy are used to evaluate the performance of the systems.
Image Processing in Diabetic Related Causes by Amit Kumar, Fahimuddin Shaik