Soft fuzzy rough set-based MR brain image segmentation

Namburu, Anupama; Kumar, S. Srinivas; Edara, Srinivasa Reddy

doi:10.1016/j.asoc.2016.08.020

Cited by 58 publications

(19 citation statements)

References 30 publications

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“…Küçükkülahlı [22] and Namburu [23] identified the number of cluster values in the clustering algorithm using the peak value of the histogram of an image. In [22], the automatic segmentation method using the histogram-based k-means clustering algorithm was developed.…”

Section: Related Literaturementioning

confidence: 99%

“…In [22], the automatic segmentation method using the histogram-based k-means clustering algorithm was developed. In [23], the soft fuzzy rough c-means clustering algorithm (SFRCM) was used to segment the MRI brain tumor images. The proposed SRFCM algorithm achieved a better Jaccard coefficient value of 0.97 for without noise and 0.79 for with 9% Gaussian noise when compared with the existing clustering algorithms namely, k-means, rough k-means (RKM), rough fuzzy c-means (RFCM), and generalized rough c-means (GFCM).…”

Section: Related Literaturementioning

confidence: 99%

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Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm

Inbarani

Azar

Ganesan³

2020

Electronics

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Segmenting an image of a nucleus is one of the most essential tasks in a leukemia diagnostic system. Accurate and rapid segmentation methods help the physicians identify the diseases and provide better treatment at the appropriate time. Recently, hybrid clustering algorithms have started being widely used for image segmentation in medical image processing. In this article, a novel hybrid histogram-based soft covering rough k-means clustering (HSCRKM) algorithm for leukemia nucleus image segmentation is discussed. This algorithm combines the strengths of a soft covering rough set and rough k-means clustering. The histogram method was utilized to identify the number of clusters to avoid random initialization. Different types of features such as gray level co-occurrence matrix (GLCM), color, and shape-based features were extracted from the segmented image of the nucleus. Machine learning prediction algorithms were applied to classify the cancerous and non-cancerous cells. The proposed strategy is compared with an existing clustering algorithm, and the efficiency is evaluated based on the prediction metrics. The experimental results show that the HSCRKM method efficiently segments the nucleus, and it is also inferred that logistic regression and neural network perform better than other prediction algorithms.

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Section: Related Literaturementioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm

Inbarani

Azar

Ganesan³

2020

Electronics

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“…If grouping of pixel inside the box is over, the area within the contour will be extracted and displayed. In this work, CV is utilized to mine the irregular section (tumor) from preprocessed brain MRI of Flair and T2 modality [15,46].…”

Section: 2mentioning

confidence: 99%

Jaya Algorithm Guided Procedure to Segment Tumor from Brain MRI

Satapathy

Rajinikanth

2018

Journal of Optimization

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Brain abnormality is a cause for the chief risk factors in human society with larger morbidity rate. Identification of tumor in its early stage is essential to provide necessary treatment procedure to save the patient. In this work, Jaya Algorithm (JA) and Otsu’s Function (OF) guided method is presented to mine the irregular section of brain MRI recorded with Flair and T2 modality. This work implements a two-step process to examine the brain tumor from the axial, sagittal, and coronal views of the two-dimensional (2D) MRI slices. This paper presents a detailed evaluation of thresholding procedure with varied threshold levels (Th=2,3,4,5), skull stripping process before/after the thresholding practice, and the tumor extraction based on the Chan-Vese approach. Superiority of JA is confirmed among other prominent heuristic approaches found in literature. The outcome of implemented study confirms that Jaya Algorithm guided method is capable of presenting superior values of Jaccard-Index, Dice-Coefficient, sensitivity, specificity, accuracy, and precision on the BRATS 2015 dataset.

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“…Wang [29] investigated characterizations of generalised fuzzy-rough sets in the core rough equalities context. Namburu et al [32] suggested a soft fuzzy-rough set-based segmentation of magnetic resonance brain image for handling the uncertainty regarding the indiscernibility and vagueness in a parameterized representation. Wang [30] examined the topological structures of L-fuzzyrough set theory.…”

Section: Related Workmentioning

confidence: 99%

“…Pan et al [31] enhanced the fuzzy preference relation rough set model with an additive consistent fuzzy preference relation. Namburu et al [32] suggested the soft fuzzy-rough set-based magnetic resonance brain image segmentation for handling the uncertainty related to indiscernibility and vagueness. Li et al [33] proposed an effective fuzzy-rough set model for feature selection.…”

Section: Introductionmentioning

confidence: 99%

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

et al. 2017

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Rough set theory has been used extensively in fields of complexity, cognitive sciences, and artificial intelligence, especially in numerous fields such as expert systems, knowledge discovery, information system, inductive reasoning, intelligent systems, data mining, pattern recognition, decision-making, and machine learning. Rough sets models, which have been recently proposed, are developed applying the different fuzzy generalisations. Currently, there is not a systematic literature review and classification of these new generalisations about rough set models. Therefore, in this review study, the attempt is made to provide a comprehensive systematic review of methodologies and applications of recent generalisations discussed in the area of fuzzy-rough set theory. On this subject, the Web of Science database has been chosen to select the relevant papers. Accordingly, the systematic and metaanalysis approach, which is called "PRISMA," has been proposed and the selected articles were classified based on the author and year of publication, author nationalities, application field, type of study, study category, study contribution, and journal in which the articles have appeared. Based on the results of this review, we found that there are many challenging issues related to the different application area of fuzzy-rough set theory which can motivate future research studies.

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Soft fuzzy rough set-based MR brain image segmentation

Cited by 58 publications

References 30 publications

Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm

Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm

Jaya Algorithm Guided Procedure to Segment Tumor from Brain MRI

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

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