Segmenting MRI brain images using evolutionary computation technique

Kumar, S. V. Aruna; Harish, B. S.; Guru, D. S.

doi:10.1109/ccip.2015.7100707

Cited by 7 publications

(5 citation statements)

References 35 publications

(23 reference statements)

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“…The proposed consensus clustering method consists of a combination of traditional fuzzy sets and intuitionistic sets to not only increase the robustness of the noise but also use the neighborhood information when forming the clusters. To do so, we use the Robust Spatial Kernel FCM (RSKFCM) [28] and Generalized Spatial Kernel FCM (GSKFCM) [29] methods alongside the two variants of the Modified Intuitionistic Fuzzy C-Means [20] technique. Finally, we fuse the results of the clustering methods using a voting schema.…”

Section: Segmentationmentioning

confidence: 99%

“…Robust Spatial Kernel Fuzzy C-Means (RSKFCM) [28] is the variant of conventional Fuzzy C-Means (FCM). RSKFCM addresses the noise sensitivity and neighborhood information ignorance limitations of FCM.…”

Section: Robust Spatial Kernel Fcm (Rskfcm)mentioning

confidence: 99%

“…The two clustering methods are based on a traditional fuzzy set and the other two are based on an intuitionistic set. In the traditional fuzzy set category, Robust Spatial Kernel FCM (RSKFCM) [28] and Generalized Spatial Kernel FCM (GSKFCM) [29] are employed. On the other hand, in the intuitionistic fuzzy set category, two variants of Modified Intuitionistic Fuzzy C-Means [20] are employed.…”

Section: Introductionmentioning

confidence: 99%

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A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation

Kumar¹,

Yaghoubi

Proença

2022

Applied Sciences

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Brain tissue segmentation is an important component of the clinical diagnosis of brain diseases using multi-modal magnetic resonance imaging (MR). Brain tissue segmentation has been developed by many unsupervised methods in the literature. The most commonly used unsupervised methods are K-Means, Expectation-Maximization, and Fuzzy Clustering. Fuzzy clustering methods offer considerable benefits compared with the aforementioned methods as they are capable of handling brain images that are complex, largely uncertain, and imprecise. However, this approach suffers from the intrinsic noise and intensity inhomogeneity (IIH) in the data resulting from the acquisition process. To resolve these issues, we propose a fuzzy consensus clustering algorithm that defines a membership function resulting from a voting schema to cluster the pixels. In particular, we first pre-process the MRI data and employ several segmentation techniques based on traditional fuzzy sets and intuitionistic sets. Then, we adopted a voting schema to fuse the results of the applied clustering methods. Finally, to evaluate the proposed method, we used the well-known performance measures (boundary measure, overlap measure, and volume measure) on two publicly available datasets (OASIS and IBSR18). The experimental results show the superior performance of the proposed method in comparison with the recent state of the art. The performance of the proposed method is also presented using a real-world Autism Spectrum Disorder Detection problem with better accuracy compared to other existing methods.

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Section: Segmentationmentioning

confidence: 99%

Section: Robust Spatial Kernel Fcm (Rskfcm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation

Kumar¹,

Yaghoubi

Proença

2022

Applied Sciences

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“…Kumar et.al [46] has proposed a system of segmenting the MRI of a brain image using the evolutionary computational technique. This paper thus concludes that the Robust Spatial Kernelled FCM (RSKFCM) with genetic algorithm provides better results than the other FCM methods.…”

Section: Evolutionary Algorithmmentioning

confidence: 99%

A Survey on Advanced Segmentation Techniques for Brain MRI Image Segmentation

Nilakant

Menon

Vikram

2017

International Journal on Advanced Science, Engineering and Information Technology

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This paper presents a survey of advanced methods for segmenting the MRI (Magnetic Resonance Imaging) image of the brain. Segmentation of the brain is a challenging task because it requires more emphasized methods to differentiate each of the regions present in the brain image. The intensity differences between the different regions in the brain MRI image are very less, making it difficult to automate the entire segmentation process. Hence, a thorough understanding of the existing segmentation algorithm is essential for accurate segmentation. The segmentation algorithms surveyed in this work are Neural Network Model, Self-Organizing Maps, Radial Basis Function, Back Propagation, Fuzzy C-Means, Deformable Models, Level Set Models, Genetic Algorithm, Differential Evolutionary Algorithm, Hybrid Clustering and Artificial Intelligence. Such a survey would be helpful for researchers working in the field of brain image segmentation. The paper discusses the complexities in the segmentation algorithm and also the challenges in segmenting the brain MRI images. The segmentation outputs and analysis of the existing literature has also been discussed. The major criteria and their advantages in the segmentation of each algorithm have been reported accordingly in the observations.

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“…Wang et al, [7] proposed a method for composite image segmentation by integrating local statistical analysis and the overall similarity measurement for constructing the energy function by utilizing by Level Set Method. Aruna Kumar et al, [6] proposed a new approach for Segmentation of MRI brain images using evolutionary computation technique which is based on the genetic algorithm based on RSKFCM. RSKFCM genetic algorithm initializes the centers of a cluster and attains the global minima of the objective function.…”

Section: Introductionmentioning

confidence: 99%

An enhanced variational level set method for MRI brain image segmentation using IFCM clustering and LBM

Duth¹,

Kulkarni²

2018

IJET

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Today's technological advances in medical imaging have given rise to efficient diagnostic procedures. Segmentation identifies and defines individual objects with various attributes such as size, shape, texture, spatial location, contrast, brightness, noise, and context. Deformable segmentation methods are Active contours, which are used to match and track images of an atomic structure by determining constraints derived from the image data. Level set method is an integral part of active contour family, considerable work towards level set methods has identified two main disadvantages i.e., initialization of controlling parameters and time complexity. In this paper, the methodology employed proposes an enhanced Variational level set methodology for Magnetic Resonance (MR) brain image segmentation with heterogeneous intensity. Core concept of IFCM is based on Intuitionistic fuzzy set. Both the values of membership and non membership values for the purpose of labelling are utilized together. As the result of experimentation reveals the efficiency of the recommended IFCM algorithm and Lattice Boltzmann Method (LBM) to overcome the drawbacks of Level Set methods by using the energy function to reduce the processing time which addresses the time complexity issue. The proposed system combines of both IFCM and LBM to form a novel approach. The system is tested on a large set of MRI brain images, extensive research and experiments were carried over on the standard dataset and the results are found to be improved in identification of tumor size detection with respect to time complexity.

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Segmenting MRI brain images using evolutionary computation technique

Cited by 7 publications

References 35 publications

A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation

A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation

A Survey on Advanced Segmentation Techniques for Brain MRI Image Segmentation

An enhanced variational level set method for MRI brain image segmentation using IFCM clustering and LBM

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