This work proposes an approach to detect the presence of ischemic lesion in the tissue part of the brain. Accurate classification and segmentation of stroke affected regions are essential for quick diagnosis. Image classification is an important step for high-level processing of automatic brain stroke classification. The proposed method employs Skull Elimination Algorithm (SEA), Central Line Sketching Algorithm (CLSA), Fuzzy C-means (FCM) clustering-based segmentation and Discrete Orthonormal Stockwell Transform (DOST). The skull elimination and CLSAs are the main stages of preprocessing. The skull elimination was mainly adopted for extracting only the tissue part in the brain and the CLSA is used for splitting the Magnetic Resonance Image (MRI) into two equal sections. FCM-based segmentation is mainly used for extracting the lesion part. Then in the next stage DOST is applied into left and right sections of brain image for extracting the features such as mean, median and standard deviation which classifies the normal and abnormal MRI. Copyright © 2016 Inderscience Enterprises Ltd.

Menaka R

Electronics

School of Electronics Engineering

Chennai Campus

Jayaram P.V

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

&nbsp;

&nbsp;

VIT University

International Journal of Biomedical Engineering and Technology

This work introduces an efficient approach for brain tumour detection using curvelet transform-based statistical features combined with GLCM (Grey Level Cooccurrence Matrix) texture features. The detection of the brain tumour is considered as a challenging problem, due to the irregularity of the highly varying structure of the tumour cells. The major contribution of the proposed work resides in the selection of significant features from both spatial and frequency domains for training the system. It combines the curvelet transform-based statistical features in the frequency domain with the GLCM texture features in the spatial domain. The proposed method applies skullstripping as the pre-processing step to extract the brain portion from the MRI slice. This pre-processed image is subjected to watershed transform-based segmentation process to extract the necessary region of interest. From the extracted region of interest, frequency and spatial domain-based features are extracted. Finally, the classification model is developed using support vector machine. Experiments reveal that the proposed classifier is good in terms of accuracy. Copyright © 2015 Inderscience Enterprises Ltd.

A comprehensive framework for classification of brain tumour images using SVM and curvelet transform

IEEE Transactions on Visualization and Computer Graphics

Exploring Brushlet Based 3D Textures in Transfer Function Specification for Direct Volume Rendering of Abdominal Organs

Procedia Computer Science

Discrete Orthonormal Stockwell Transform Based Feature Extraction for Pose Invariant Face Recognition

Medical Image Analysis

Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images

Computers & Electrical Engineering

Classification of mental tasks using stockwell transform

An experimental study of Stockwell transform-based feature extraction method for ischemic stroke detection

Journal	International Journal of Biomedical Engineering and Technology
Publisher	Inderscience Publishers
ISSN	1752-6418
Open Access	No