The segmentation, as well as analysis of renal tumor, is important to step which is performed by the doctor while deciding the stage of cancer and finding the appropriate method of its treatment. This paper determines a novel approach in order to develop an algorithm which helps in detecting and analysis of renal cancer tumors. The developed algorithm has been employed to segment and pre-processes the image for its better visualization and segment the visible tumor. The pre-processing has a hybrid filter for image enhancement and noise removal. An artificial neural network is also used by Hybrid Self Organizing Maps. It uses the clustering of image data to highlight the detected region. The appropriate output is obtained according to the medical field and it is compared with the resultant image to improve the algorithm. It helps in understanding the affected region in the human body and for better visualization. A region growing method is also applied for finding the same intensity images in images and to segment out the tumor from the processed image. The objective of this paper is to create a CT image database and then apply pre-processing methods on the image. The image segmentation is done by using Haar wavelet. The boundary is also detected by using canny. The feature extraction is applied to the image on the basis of shape, intensity, and texture and after that Fuzzy clustering is applied to get the optimized segmented image.
Renal tumors, Region growing, MRI, CT scan, ultrasound, ANN, Image Processing, Region Growing, SOM
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