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Computed Tomography Brain Images Semantic Segmentation

Computed Tomography Brain Images Semantic Segmentation

Poonam Fauzdar, Sarvesh Kumar
Copyright: © 2018 |Pages: 28
ISBN13: 9781522528487|ISBN10: 1522528482|EISBN13: 9781522528494
DOI: 10.4018/978-1-5225-2848-7.ch004
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MLA

Fauzdar, Poonam, and Sarvesh Kumar. "Computed Tomography Brain Images Semantic Segmentation." Handbook of Research on Advanced Concepts in Real-Time Image and Video Processing, edited by Md. Imtiyaz Anwar, et al., IGI Global, 2018, pp. 77-104. https://doi.org/10.4018/978-1-5225-2848-7.ch004

APA

Fauzdar, P. & Kumar, S. (2018). Computed Tomography Brain Images Semantic Segmentation. In M. Anwar, A. Khosla, & R. Kapoor (Eds.), Handbook of Research on Advanced Concepts in Real-Time Image and Video Processing (pp. 77-104). IGI Global. https://doi.org/10.4018/978-1-5225-2848-7.ch004

Chicago

Fauzdar, Poonam, and Sarvesh Kumar. "Computed Tomography Brain Images Semantic Segmentation." In Handbook of Research on Advanced Concepts in Real-Time Image and Video Processing, edited by Md. Imtiyaz Anwar, Arun Khosla, and Rajiv Kapoor, 77-104. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2848-7.ch004

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Abstract

In this paper we applianced an approach for segmenting brain tumour regions in a computed tomography images by proposing a multi-level fuzzy technique with quantization and minimum computed Euclidean distance applied to morphologically divided skull part. Since the edges identified with closed contours and further improved by adding minimum Euclidean distance, that is why the numerous results that are analyzed are very assuring and algorithm poses following advantages like less cost, global analysis of image, reduced time, more specificity and positive predictive value.

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