Classification Human Brain Images and Detection Suspicious Abnormal Area
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2016, Vol 18, Issue 3
Abstract
Abstract: Magnetic Resonance Images (MRI) are widely utilized in the diagnosis of brain images. In this study we have developed a new approach for automatic classification of the normal and abnormal MRI images. Theproposed method consists of four stages namely preprocessing, transformation, texture feature extraction and classification. The first stage is preprocessing image and utilized filter is applied for noise reduction and tomake the image suitable for transformation. In the second stage, is utilized Discrete Multiwavelet Transform (DMWT) to reduce the dimensionality. In the third stage, features extraction based combined between first order statics (FOS) and second order statics (SOS). The fourth stage in the classification stage, a supervised probabilistic neural network (PNN) classifier is utilized to classify the experimental images into normal and abnormal. Finally, proposed algorithm is to segmenting, hexagonal superpixel algorithm. The basic idea in this proposed algorithm is how to detection suspicious abnormal (tumor) area brain to reduce computational time for clinical high diagnostic. It was found this algorithm was very efficient segmentation image, which can be very powerful in the biomedical field of tumor classification.
Authors and Affiliations
Ata'a A. Hasan , Dr. Dhia A. Jumaa
Keywords
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