A novel intuitionistic fuzzy set approach for segmentation of kidney MR images

Shreyas Mushrif, Aldo Morales, Christopher Sica, Qing X. Yang, Susan Eskin, Lawrence Sinowa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a novel algorithm, which uses intuitionistic fuzzy sets and rough set theory to segment the renal components in kidney MR images. A new membership function is proposed and then is used to obtain an intuitionistic fuzzy model of the image to compensate the inherent heterogeneity present among the different renal tissue classes. In addition, a new method, which uses Hamming distance is proposed to calculate the histon. The histon is then used to compute intuitionistic fuzzy roughness measure which yields optimum valley points for image segmentation. The proposed algorithm segments the kidney MR images into medulla, cortex, and blood vessels. The quantitative performance evaluation indicates better performance of the proposed algorithm over a competing technique.

Original languageEnglish (US)
Title of host publication2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509067138
DOIs
StatePublished - Feb 7 2017
Event2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Philadelphia, United States
Duration: Dec 3 2016 → …

Publication series

Name2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings

Other

Other2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016
CountryUnited States
CityPhiladelphia
Period12/3/16 → …

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All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering

Cite this

Mushrif, S., Morales, A., Sica, C., Yang, Q. X., Eskin, S., & Sinowa, L. (2017). A novel intuitionistic fuzzy set approach for segmentation of kidney MR images. In 2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings [7846874] (2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPMB.2016.7846874