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Edge Detection Algorithms Using Brain Tumor Detection and Segmentation Using Artificial Neural Network Techniques

Journal: International Research Journal of Advanced Engineering and Science (IRJAES) (Vol.1, No. 3)

Publication Date:

Authors : ; ;

Page : 135-140

Keywords : Artificial neural network (ANN); edge detection; image segmentation; brain tumor detection; histogram thresholding.;

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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 research, 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 research, 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 research presents two techniques for the detection purpose; first one is Histogram Thresholding and second is Artificial Neural Network technique. The Edge detection segmentation of brain tissue in the magnetic resonance image (MRI) is very important for detecting and existence of outlines the brain tumor. In this research an algorithm for segmentation based on the symmetry character of brain image is presented. Our goal is to detect the position and edge of tumors automatically. Experiments were carried on real pictures, and the results show that the algorithm is flexible and convenient. This proposed method is more efficient and faster to identify the detecting the tumor region from T1, T2-weighted MRI brain images. 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.

Last modified: 2016-09-25 22:42:33