Abstract
The brain is one of the most important organs in the human body because it is its control centre, and any disease of the brain is a real threat to human life. A brain tumour is a newly formed, foreign structure, whose growth causes an increase in intracranial tightness. A stroke is defined as a sudden neurological deficit caused by central nervous system ischemia or haemorrhage. CT is a routine examination when these diseases are suspected. However, the distinction between stroke and tumour is not always straightforward, even for experienced radiologists. There are solutions for detecting each disease separately, but there is no system that would support distinguishing of both diseases. Therefore, the aim of this work is to develop a system that allows discrimination between a stroke and a brain tumour on CT images based on the analysis of the texture features calculated for the region of interest marked by radiologist. Next, feature selection was performed by Fisher criterion and convex hull approach. Finally, selected features were classified with use of the Classification Learner application available in MATLAB R2021b. Classification methods based on classification trees, k-nearest neighbours (KNN), and support vector machine (SVM) gave the best classification results. It was demonstrated that classification accuracy reached over 95% for the analysed feature set that is promising result in semiautomatic discrimination between stroke and tumour in CT data.
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Acknowledgement
We would like to thank to Professor Andrzej Klimek, former head of the Department of Neurology and Strokes, Medical University of Lodz who initiated this study and provided CT data for analysis.
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Kobus, M., Sobczak, K., Jangas, M., Świątek, A., Strzelecki, M. (2022). Discrimination Between Stroke and Brain Tumour in CT Images Based on the Texture Analysis. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2022. Advances in Intelligent Systems and Computing, vol 1429. Springer, Cham. https://doi.org/10.1007/978-3-031-09135-3_15
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