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Texture Segmentation and Features of Medical Images

Texture Segmentation and Features of Medical Images

Ashwani Kumar Yadav, Vaishali, Raj Kumar, Archek Praveen Kumar
Copyright: © 2021 |Pages: 20
ISBN13: 9781799827429|ISBN10: 1799827429|EISBN13: 9781799827436
DOI: 10.4018/978-1-7998-2742-9.ch023
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MLA

Yadav, Ashwani Kumar, et al. "Texture Segmentation and Features of Medical Images." Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning, edited by Geeta Rani and Pradeep Kumar Tiwari, IGI Global, 2021, pp. 450-469. https://doi.org/10.4018/978-1-7998-2742-9.ch023

APA

Yadav, A. K., Vaishali, Kumar, R., & Kumar, A. P. (2021). Texture Segmentation and Features of Medical Images. In G. Rani & P. Tiwari (Eds.), Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning (pp. 450-469). IGI Global. https://doi.org/10.4018/978-1-7998-2742-9.ch023

Chicago

Yadav, Ashwani Kumar, et al. "Texture Segmentation and Features of Medical Images." In Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning, edited by Geeta Rani and Pradeep Kumar Tiwari, 450-469. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-2742-9.ch023

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Abstract

Texture analysis is one of the basic aspects of human visual system by which one can differentiate the objects and homogenous areas in an image. Manual diagnosis is not possible for huge database of images. Automatic diagnosis is required for greater accuracy in a shorter time. Texture analysis is required for effective diagnosis of medical images like functional MRI (magnetic resonance image) and diffusion tensor MRI, where only visualization is not sufficient to get the pathological information. This chapter explains the basic concepts of texture analysis and features available for analysis of medical images. Specifically, the intense review of texture segmentation and texture feature extraction and entropy measures of medical images have been done. The chapter also explores the available techniques for it. Common findings, comparative analysis, and gaps identified have also been mentioned on both issues.

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