Brain Tumor Detection with Adaptive Medical Image Deep Color Algorithm

VijiyaKumar, K.; Kaarthik, R; Yoganandhan, M; Yuvaraja, P

doi:10.1109/icscan53069.2021.9526380

Cited by 2 publications

(1 citation statement)

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“…Another research series based on PCM and evidence theory presented algorithms for information clustering with evidential c-means (ECM) being the only algorithm used for brain MRI segmentation in [25][26][27]. Despite their flexibility, these algorithms had practical limitations in image segmentation, including the non-use of neighborhood information and the need for multiple MRI images, making them less suitable for brain MRI segmentation.…”

Section: Fuzzy Clustering In Brain Mri Segmentationmentioning

confidence: 99%

A Systematic Review of Brain MRI Segmentation and Uncertainty Modeling Using Evidence Theory with Implementation of Fuzzy Clustering and Fuzzy Inference Systems Methods

Sedeeq,

Aljobouri,

Hasan

2023

RIA

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Image segmentation, the process of partitioning an image into its constituent parts, is a pivotal step in image processing, particularly with respect to brain MRI images. This operation's complexity is magnified due to inherent uncertainties, which may arise from factors such as noise and intensity non-uniformity. In this study, a systematic review of both non-fuzzy and fuzzy medical image segmentation methods, with a focus on brain MRI images, was undertaken. Practical application of fuzzy clustering and fuzzy inference systems were demonstrated using freely accessible simulated data. This paper presents an emphasis on uncertainty modeling techniques, highlighting the potential of belief structure integration as a significant approach for future hybrid medical image segmentation techniques. The fusion of diverse information sources through this combination is posited to enhance both the accuracy and robustness of uncertainty handling, critical aspects in medical image analysis.

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Section: Fuzzy Clustering In Brain Mri Segmentationmentioning

confidence: 99%