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

ABSTRACT

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.

KEYWORDS:

• Ischaemic stroke
• CT scan images
• midline sketching
• histogram bin
• stroke lesion

Note

The authors have obtained the relevant permissions to reproduce the patient CT brain scans.

Acknowledgement

The authors wish to thank Dr. C. Emmanuel, Director, Global Hospitals for his assistance in proving the dataset. The authors extend their thanks to Dr. D.S. Halprashanth, Neurologist for his suggestions and valuable comments in completing this work.

Disclosure statement

No potential conflict of interest was reported by the authors.