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A New Region Growing Medical Image Segmentation Algorithm Based on Interval Type-2 Fuzzy Sets

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Web, Artificial Intelligence and Network Applications (WAINA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1150))

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Abstract

Segmentation of regions of interest plays important role in computer aided brain medical image diagnosis. Fuzzy techniques are widely used for this purpose as they can handle with imprecise or vague image information. The major achievement of this research is the introduction of a new region growing segmentation technique for which any fuzzy control model is possible to be applied. This enables the combination of physician knowledge, easily represented by fuzzy rules and therefore generalized, with the main concept of fuzzy control. Our approach is capable to segment cerebrospinal fluid in the cortical and subcortical areas of the brain. The study was performed by applying the segmentation method proposed on a dataset of 228 computed tomography scans of patients with diagnosed Alzheimer disease.

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Authors and Affiliations

  1. Faculty of Computer Science and Management, Wroclaw University of Science and Technology, 50-370, Wroclaw, Poland

    Martin Tabakov & Bartosz Jablonski

Authors
  1. Martin Tabakov
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  2. Bartosz Jablonski
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Corresponding author

Correspondence to Martin Tabakov .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Electrical Engineering and Information Technology, University of Naples “Frederico II”, Naples, Italy

    Flora Amato

  3. Department of Political Science, University of Campania Luigi Vanvitelli, Caserta, Italy

    Francesco Moscato

  4. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

  5. Department of Advanced Sciences, Hosei University, Tokyo, Japan

    Makoto Takizawa

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Tabakov, M., Jablonski, B. (2020). A New Region Growing Medical Image Segmentation Algorithm Based on Interval Type-2 Fuzzy Sets. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2020. Advances in Intelligent Systems and Computing, vol 1150. Springer, Cham. https://doi.org/10.1007/978-3-030-44038-1_121

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