Segmentation of blood clot MRI images using intuitionistic fuzzy set theory

Albashah, N.L.S.B. and Asirvadam, V.S. and Dass, S.C. and Meriaudeau, F. (2019) Segmentation of blood clot MRI images using intuitionistic fuzzy set theory. [["eprint_typename_conference\_item" not defined]]

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....


This paper presents the segmentation method to differentiate between hard and soft clot regions of blood clots in ischemic stroke patients. This is important in planning treatment options for stroke patients as the nature of the clot determines the type of treatment. The gradient echo sequence is used to produce MRI datasets. The MRI blood clot image consists of hard and soft clot regions which have different intensity values. However, the blood clot regions are adjacent to each other, not homogeneous and have unclear boundaries which are the main problems when segmenting the regions. Intuitionistic fuzzy fusion methodology is used to help enhance the noisy image and is shown to lead good segmentation result while using the spatial version of intuitionistic fuzzy c-mean clustering with dice coefficient of 0.8270 and 0.819 for hard clot region and soft clot region respectively. © 2018 IEEE

Item Type:["eprint_typename_conference\_item" not defined]
Impact Factor:cited By 1
Uncontrolled Keywords:Biomedical engineering; Blood; Fuzzy set theory; Fuzzy sets; Image enhancement; Image fusion; Magnetic resonance imaging; Patient treatment, Dice coefficient; Gradient echo sequences; Intuitionistic fuzzy; Intuitionistic Fuzzy C-Means; Intuitionistic fuzzy sets; Ischemic strokes; Segmentation methods; Segmentation results, Image segmentation
ID Code:23544
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:19 Aug 2021 07:57
Last Modified:19 Aug 2021 07:57

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