Biology 代写:Build A Decision Support System Using Retinal Images
Anantha et al. have implemented an algorithms for automatic detection of exudates by using dynamic thresholding and edge detection (IDTED). In first step, green component of the pre-processed image was divided into blocks of 64×64 pixels with 50% overlap with each other. A dynamic thresholding was selected based on the histogram of each blocks. High threshold value is set if the histogram was unimodal, else threshold value can be find using Otsuâ€™s thresholding algorithms. As each pixels belongs to four blocks, a particular pixels was classified as an exudates pixels if its intensity value was higher than the interpolate of threshold value of the four blocks to which it belongs. The result of this classification process was a binary image. The third step was edges detection which aim to detect all objects with sharp edges included exudates by using Canny edge detector. A binary image containing sharp edges was obtained from thresholding using a defined global threshold value. The last step was false positive region elimination. The binary images obtained from step 2 and 3 were combined by a feature based AND. Pixels that ON in both image were marked as white in a new binary image and became the exudates region. The IDTED algorithm was tested against 25 digital retinal images and compared with the performance of a human grader, has shown a mean sensitivity of 99% and a mean predictivity of 93%.
Blood Vessels Detection
Proposed Solution And Implementation Plan / Design
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