ArticleFrontiers in human neuroscience2023
Design of a medical decision-supporting system for the identification of brain tumors using entropy-based thresholding and non-local texture features.
Article in Frontiers in human neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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4 authors at 4 institutions in 2 countries.
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Abstract
Introduction: 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. Methods: This paper attempts to develop a new medical decision-support system for detecting and differentiating brain tumors from MR images. In the implemented approach, initially, we improve the contrast and brightness using the tuned single-scale retinex (TSSR) approach. Then, we extract the infected tumor region(s) using maximum entropy-based thresholding and morphological operations. Furthermore, we obtain the relevant texture features based on the non-local binary pattern (NLBP) feature descriptor. Finally, the extracted features are subjected to a support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), and GentleBoost (GB). Results: The presented CAD model achieved 99.75% classification accuracy with 5-fold cross-validation and a 91.88% dice similarity score, which is higher than the existing models. Discussions: By analyzing the experimental outcomes, we conclude that our method can be used as a supportive clinical tool for physicians during the diagnosis of brain tumors.
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