Among the various brain diseases, stroke is the major cause of death worldwide, next to heart attack. This paper proposes an algorithm in predicting the ischaemic stroke lesion using midline sketching and histogram bin-based technique. The visible ischaemic stroke lesion region and the normal region of the same computed tomography image are segmented with the help of histogram bins and the features are extracted. The first- and second-order statistical features for both regions are analysed. The differences in the features are utilised to categorise the lesion and non-lesion region. The statistical t-test analysis-based observations with a confidence interval of 95% for each feature are tabulated. These observations indicate that among the nine features, as per the statistical analysis, six features provide the clear differentiation between normal and abnormal regions. © 2017 The Royal Photographic Society.

Menaka R

Electronics

School of Electronics Engineering

Chennai Campus

Kanchana R

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.

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VIT University

A novel approach for characterisation of ischaemic stroke lesion using histogram bin-based segmentation and gray level co-occurrence matrix features

The Imaging Science Journal

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.

International Journal of Biomedical Engineering and Technology

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

Computer Methods and Programs in Biomedicine

Different medical data mining approaches based prediction of ischemic stroke

Medical Image Analysis

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

NeuroImage: Clinical

Automated delineation of stroke lesions using brain CT images

Radiological Physics and Technology

An automated detection method for the MCA dot sign of acute stroke in unenhanced CT

Journal	The Imaging Science Journal
Publisher	Informa UK Limited
ISSN	1368-2199
Open Access	No