KIDNEY STONE DETECTION USING IMAGE ENHANCEMENT TECHNIQUES

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Sutrave Sudharshan Rao, Ankith Jason Manohar, Dr.G.Seshikala, Sujay.S, R Aishwarya

Abstract

A kidney stone termed as renal calculi is a solid piece of material that forms in a kidney when substances that are normally found in the urine become highly concentrated. To detect these kidney stones from the X-ray image we reduce the noise by filtering the image and adjust the contrast of the image by using histogram equalization where it spreads out the most frequent pixel intensity values or stretches out the intensity range of the image. The processed image undergoes thresholding level segmentation followed by segmentation using watershed Algorithm. The implementation of proposed technique is done using open CV and python. The image obtained as a result of watershed algorithm undergoes Contour Segmentation where it iterates through each contour and computes the Bounding Rectangle followed by approximate Poly DP contour technique which approximates a contour shape to another shape with less number of vertices depending upon the precision we specify. Lastly gamma transformation is applied, where overall brightness of an image is controlled by expanding the range of intensity levels in an image so that it spans the full intensity of the captured image. As a result, it has been determined that the proposed approach gives fast, accurate and reliable results with around 90 percent accuracy for classification and about 6 percent error in stone detection targets.

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