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Design of a medical decision-supporting system for the identification of brain tumors using entropy-based thresholding and non-local texture features

Publication Type : Journal Article

Source : Frontiers in Human Neuroscience, 17, 1157155

Url : https://www.frontiersin.org/articles/10.3389/fnhum.2023.1157155/full

Campus : Amaravati

School : School of Engineering

Year : 2023

Abstract : Brain tumors arise due to abnormal growth of cells at any brain location with uneven boundaries and shapes. Usually, they proliferate rapidly, and their size increases by approximately 1.4% a day, resulting in invisible illness and psychological and behavioral changes in the human body. It is one of the leading causes of the increase in the mortality rate of adults worldwide. Therefore, early prediction of brain tumors is crucial in saving a patient’s life. In addition, selecting a suitable imaging sequence also plays a significant role in treating brain tumors. Among available techniques, the magnetic resonance (MR) imaging modality is widely used due to its noninvasive nature and ability to represent the inherent details of brain tissue. Several computer-assisted diagnosis (CAD) approaches have recently been developed based on these observations. However, there is scope for improvement due to tumor characteristics and image noise variations. Hence, it is essential to establish a new paradigm.

Cite this Research Publication : Reddy, K. R., Batchu, R. K., Polinati, S., & Bavirisetti, D. P. (2023). Design of a medical decision-supporting system for the identification of brain tumors using entropy-based thresholding and non-local texture features. Frontiers in Human Neuroscience, 17, 1157155.

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