Segmented disk image explanation first step is to

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'segmented disk image' ); Explanation: First step is to load the image and convert it into the required domain i.e. RGB or HSI. For each of image domain an image is divided into three layers and processing is done on each layer separately. For RGB model Red, Green, Blue layers are extracted. If thresholding is done then the value of threshold will vary from layer to layer. If k-means clustering is done then important factor is no of clusters. In my code the number of clusters is defined by number of colors. In the given problem the number of clusters should be 3. One is the background, one in the foreground and one is the object within foreground i.e. retinal disk. Our point of concern is only the retinal disk. I have used inbuilt function of Matlab K-means for which the input arguments are No of clusters No of times the process repeats Which type of distance to be calculated Due to veins structure within disk the discontinuity occurs so to remove this discontinuity I have applied imerode and imdilate command. The erode command will select the area of disk by setting radius value in its parameter and imdilate command will dilate the selected area.
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Limitations: This technique does not retain the exact shape of desired object especially for images which have less gray level differences from their neighboring pixels because if there is less difference in gray level and thresholding is applied after smoothing either it will included the unwanted surrounding pixels or it will discard the wanted pixels.
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