Indian Journal of Science and Technology

Abstract

Objectives: To segment tumor with higher accuracy. Methods/Statistical Analysis: Noise removal is done with the help of Gabor filter as a preprocessing step. Skull stripping is done to remove non cerebral regions using thresholding and morphological operations. Segmentation using watershed algorithm is done, as it achieves exact location of outline. Unsupervised type of neural network i.e. self organizing maps is used for classification. Finding: It has been analyzed that by combining watershed and neural networks segmentation accuracy has been improved to 95.93%. The motive of the research is to segment the tumor with precision using computerized segmentation algorithm that can help physicians to analyze brain diseases and treatment can be started as soon as possible. Applications: The proposed technique can be used in image processing of brain tumor detection.

Keywords: Brain Tumor Segmentation, Image Segmentation, Magnetic Resonance Imaging (MRI), Self Organizing Maps (SOM), Stationary Wavelet Transform (SWT)