An Effective Brain Tumor Detection and Segmentation Technique by Using Watershed Algorithm and Optimized Neural Network
In recent years brain tumor detection using MRI images is an effective clinical research area since MRI images does not make any tissue damage with its radiation and provides useful information about the tissue we are using MRI images in our proposed work for the brain tumor detection.
Our proposed brain tumor detection method consists of four sections, i.e., pre-processing, segmentation, feature extraction and classification. Initially the input image fetched from the MRI database will be subjected to skull stripping in order to remove the unwanted region from the image.
Then the skull stripped image is segmented using efficient watershed segmentation algorithm. Afterwards from the segmented image shape, intensity and texture features will be extracted. Then that extracted features is given as the input to the ANN classifier. Here the ANN classifier is optimized
by well-known ABC optimization technique in order to get the enhanced classification accuracy. Thus from the classified abnormal images the tumor and edema region will be separated using modified region growing algorithm. The results will be analyzed to demonstrate the performance of the proposed
segmentation and classification technique with other existing techniques.
Keywords: Artificial Bee Colony (ABC); Artificial Neural Network (ANN); Feature Extraction; Magnetic Resonance Image (MRI); Skull Stripping
Document Type: Research Article
Affiliations: 1: Narayanaguru College of Engineering, Kanyakumari District, Tamil Nadu, India 2: Lourdes Mount College of Engineering and Technology, Kanyakumari District, Tamil Nadu, India
Publication date: 01 October 2016
- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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