Brain Tumor Detection and Classification Using Clustering and Segmentation Matlab Project With Source Code

ABSTRACT

          Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm followed by morphological filtering which avoids the mis-clustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location. In this paper, we present a system based on gabor filter based enhancement technique and feature extraction techniques using texture based segmentation and SOM (Self Organization Map) which is a form of Artificial Neural Network (ANN) used to analyze the texture features extracted. SOM determines which texture feature has the ability to classify benign, malignant and normal cases. Watershed segmentation technique is used to classify cancerous region from the non cancerous region.

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Mr. Roshan P. Helonde

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