AN OPTIMISED VLSI APPROACH FOR THE TUMOR DETECTION
Author’s Name : R Dhivya devi
Volume 03 Issue 01 Year 2016 ISSN No: 2349-252X Page no: 1-6
Image segmentation is the process of dividing images according to its characteristics like color and objects present in the images. The general segmentation problem involves the partitioning of a given image into a number of homogeneous segments, such that the union of any two neighbouring segments yields a heterogeneous segment. This can further be used for surgical planning, to avoid open surgery.
The techniques used are namely image registration, edge detection, contrast enhancement, watershed segmentation and finally wavelet decomposition to find the tumor growth. Comparision of different edge detection techniques based on peak signal to noise ratio and root mean square error is performed. Finally Watershed segmentation uses the intensity as a parameter to segment the whole image data set. The results show that Watershed Segmentation found to have minimum bit error rate. All the mentioned modules and techniques have been implemented in MATLAB environment for the brain tumor detection using input MR images and the part of modules like edge detection, thresholding and high pass filter are also implemented in FPGA using Verilog in Xilinx environment, the advantage being speed enhancement and re-configurability.
Brain tumor, Magnetic resonance Imaging (MRI), Image segmentation, Watershed segmentation, wavelet decomposition, MATLAB, XILINX
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