Thursday, January 30, 2014


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 Segmentation of anatomical regions of the brain is the fundamental problem in medical image analysis. In this paper, a brain tumor segmentation method has been developed and validated segmentation on 2D MRI Data. This method can segment a tumor provided that the desired parameters are set properly. This method does not require any initialization while the others require an initialization inside the tumor. In our segmentation approach adaptive boost segmentation algorithm is used. it uses the intensity as a parameter to segment the whole image data set. The input MRI image is preprocessed and loaded into matlab workspace. In the segmentation process the image is divided into blocks depending on the edge, gray and threshold parameter. The blocks are divided by comparing the intensity value of the image with the parameters as the intensity of the tumor affected area will be higher. Likewise the tumor surface from the MRI image is segmented out. After the detection of the tumor it is then classified using adaptiveboost algorithm which gives the type of the tumor for the doctor's convenience. Here the threshold limit is applied to each image and the limit is tested on the adaptiveboost  algorithm.

Key words- AdaBoost classifier, brain Tumor Detection, Segmentation,  Fractal, MRI, Multi-FractalAnalysis,  MultiresolutionWavelet, Texture Modeling.

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