Brain Tumor Detection and Segmentation Using Artificial Neural Network Techniques

Brain tumor is one of the major causes of death among people. It is evident that the chances of survival can be increased if the tumor is detected and classified correctly at its early stage. The segmentation of brain tumors in magnetic resonance images (MRI) is a challenging and difficult task because of the variety of their possible shapes, locations, image intensities. In this paper, it is intended to summarize and compare the methods of automatic detection of brain tumor through Magnetic Resonance Image (MRI) using Histogram Thresholding and Artificial Neural Network. The proposed method can be successfully applied to detect the contour of the tumor and its geometrical dimension. Also in this paper, a modified Artificial Neural Network (ANN) model that is based on learning vector quantization with image and data analysis and manipulation techniques is proposed to carry out an automated brain tumor classification using MRI-scans. The assessment of the modified ANN classifier performance is measured in terms of the training performance, classification accuracies and computational time. MRI (Magnetic resonance Imaging) brain tumor images detection is a difficult task due to the variance and complexity of tumors. This paper presents two techniques for the detection purpose; first one is Histogram Thresholding and second is Artificial Neural Network technique. The proposed Neural Network technique consists of some stages, namely, feature extraction, dimensionality reduction, detection, segmentation and classification. In this paper, the purposed method is more accurate and effective for the brain tumor detection and segmentation. For the implementation of this proposed work we use the Image Processing Toolbox under Matlab Software.