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Arabian Journal for Science and Engineering

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28.06.2019 | Research Article - Computer Engineering and Computer Science

Brain MR Imaging Tumor Detection Using Monogenic Signal Analysis-Based Invariant Texture Descriptors

verfasst von: Deepak O. Patil, Satish T. Hamde

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 11/2019

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Abstract

Brain tumor is considered as a fatal disease with low survival rate and has the highest cost of care per patient. This article proposes a computer-assisted system for the recognition of brain tumor image through magnetic resonance imaging based on the monogenic signal analysis. From different monogenic components, textural descriptors are obtained using completed local binary pattern and gray-level co-occurrence matrix. In the pre-processing step, various filtering for noise removal and contrast enhancement techniques are implemented. Local phase, energy and orientation components originated from the monogenic signal analysis method are used for textural feature extraction. Fisher score-based filter approach for feature selection is then employed to derive the discriminating feature set. Finally, the acquired optimal feature set is classified using the support vector machine classifier. Two benchmark MR image datasets, e-health laboratory and Harvard medical laboratory, have been used to validate the system performance. Overall detection accuracy obtained was above 99%. The experimental results demonstrate the effectiveness of the proposed approach and the potential to assist the medical experts in enhancing the detection rate. Furthermore, the presented approach delivers superior performance in brain tumor image recognition as compared to existing techniques.

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Titel: Brain MR Imaging Tumor Detection Using Monogenic Signal Analysis-Based Invariant Texture Descriptors
verfasst von: Deepak O. Patil
Satish T. Hamde
Publikationsdatum: 28.06.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Arabian Journal for Science and Engineering / Ausgabe 11/2019
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-019-03989-2

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