Abstract
Classification of a brain tumor in magnetic resonance images is an important task and plays a significant role in the clinical decision process by medical experts. In real life, different brain tumors have a similar appearance and textural descriptions; therefore, diagnosing a tumor is challenging. Detection and classification of tumors manually are a time-consuming task, and sometimes it also increases the chance of misclassifications. In this article, glioma, meningioma, and pituitary brain tumor classification technique is proposed using steerable wavelet transform and enhanced local optimal-oriented pattern descriptors. The proposed Frei–Chen-based mask used in this descriptor efficiently encodes edge and line subspace information of texture by extracting the local structures; on the other hand, local binary pattern and local directional pattern descriptors encode intensity information. After decomposing the input magnetic resonance tumor image at different scales, enhanced local optimal-oriented pattern textural features are extracted from each sub-band. To reduce the dimensionality of the feature vector, the Fisher score-based feature selection is employed. Finally, a multi-class support vector machine is used to classify input magnetic resonance tumor images. An extensive set of experiments are performed to evaluate the proposed algorithm using a publicly available database. The enhanced classification rate demonstrates the presented approach’s effectiveness achieved using a new enhanced local optimal-oriented pattern descriptor compared to local binary pattern, local directional pattern, local optimal-oriented pattern, and modified local optimal-oriented pattern.
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Patil, D.O., Hamde, S.T. A New Enhanced Local Optimal-Oriented Pattern Descriptor using Steerable Wavelet Transform for MRI Brain Tumor Classification. Iran J Sci Technol Trans Electr Eng 47, 369–384 (2023). https://doi.org/10.1007/s40998-022-00557-7
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DOI: https://doi.org/10.1007/s40998-022-00557-7