A unique unsupervised enhanced intuitionistic fuzzy C-means for MR brain tissue segmentation.

Saritha Saladi, Karuna Yepuganti, Ravikumar Chinthaginjala, Tae-Hoon Kim, Shafiq Ahmad

Abstract read

In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

Explainable AI framework for skin lesion imaging based cancer detection.PloS one · 2026
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5 · Who and what money

Authors and funding

5 authors.

Saritha SaladiSchool of Electronics Engineering, VIT-AP University, Amaravathi, AP, 522237, India.

Karuna YepugantiSchool of Electronics Engineering, VIT-AP University, Amaravathi, AP, 522237, India.

Ravikumar ChinthaginjalaSchool of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, 632014, India. cvrkvit@gmail.com.

Tae-Hoon KimSchool of Information and Electronic Engineering and Zhejiang Key Laboratory of Biomedical Intelligent Computing Technology, Zhejiang University of Science and Technology, No. 318, Hangzhou, Zhejiang, China. 323020@zust.edu.cn.

Shafiq AhmadIndustrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi Arabia.

Funding

This research is funded by Zhejiang Provincial Natural Science Foundation Youth Fund Project (Grant No. LQ23F010004), the National Natural Science Youth Science Foundation Project (Grant No. 62201508) and by King Saud University (KSU) through researchers supporting project number (RSP2024R387). LQ23F010004 ; RSP2024R387

6 · The paper itself

Abstract

The human-brain is a vital and complicated organ within the body. Identifying brain-related diseases can be challenging. Typically, Magnetic Resonance Imaging (MRI) scanning methods are used to gain insights of the protected regions in the body. Brain segmentation can result in identifying region boundaries as a set of contours. However, segmenting brain images poses several challenges, including noise, bias field, and partial volume effect (PVE). Removing noise, accurately segmenting tissues and tumors are crucial for effective evaluation. To enhance tissue and tumor segmentation, a new machine learning-based method called as Gaussian-Kernelized Enhanced Intuitionistic Fuzzy-C-Means (GKEIFCM) has been proposed. Approach enhances Improved Intuitionistic Fuzzy-C-Means Algorithm (IIFCM) by utilizing Gaussian kernelized distance between pixels, resulting in uncomplicated segmentation with reduced computational times and improved efficiency. This proposed novel method proved to be expertise in tissue and tumor classification and identification respectively. The results demonstrate the effectiveness of GKEIFCM interms of Dice, Jaccard-similarity-index, Accuracy and Execution time.

Indexed as

AlgorithmsBrainFuzzy LogicMagnetic Resonance ImagingBrain NeoplasmsHumansImage Processing, Computer-AssistedBrain segmentationIntuitionistic –FCMMachine learningMRITissue classificationTumor identification

Identifiers

PMID39616246

DOI10.1038/s41598-024-81648-9

PMCPMC11608326

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.