A Modified Intuitionistic Fuzzy Clustering Algorithm for Medical Image Segmentation

MRI segmentation is critically important for clinical study and diagnosis. Existing methods based on soft clustering have several drawbacks, including low accuracy in the presence of image noise and artifacts, and high computational cost. In this paper, we introduce a new formulation of the MRI segmentation problem as a kernel-based intuitionistic fuzzy C-means (KIFCM) clustering problem and propose a new DNA-based genetic algorithm to obtain the optimal KIFCM clustering. While this algorithm searches the solution space for the optimal model parameters, it also obtains the optimal clustering, therefore the optimal MRI segmentation. We perform empirical study by comparing our method with six state-of-the-art soft clustering methods using a set of UCI (University of California, Irvine) datasets and a set of synthetic and clinic MRI datasets. The preliminary results show that our method outperforms other methods in both the clustering metrics and the computational efficiency.

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“…The corresponding decimal number is cv 2 = 1566. If the range of α is [1,15], the decoded value of α is 6.35.…”

Section: Dna Encoding and Decodingmentioning

confidence: 99%

“…Aruna et al presented a modified intuitionistic fuzzy C-means (IFCM) clustering algorithm [15], which adopts a new IFS generator and the Hausdorff distance. Verma et al presented an improved intuitionistic fuzzy C-means (IIFCM) algorithm [16], which takes the local spatial information into consideration.…”

Section: Introductionmentioning

confidence: 99%

A Kernel-Based Intuitionistic Fuzzy C-Means Clustering Using a DNA Genetic Algorithm for Magnetic Resonance Image Segmentation

Zang

Zhang

et al. 2017

Entropy

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“…In IFS, the non-membership value is computed using the fuzzy complement generator functions. In recent times, researchers have given more attention in developing IFS-based clustering methods [17][18][19][20]. Chaira [18] developed an Intuitionistic Fuzzy C-Means (IFCM) where the intuitionistic fuzzy entropy is added to the conventional FCM objective function.…”

Section: Introductionmentioning

confidence: 99%

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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“…Extensive studies have been conducted for segmenting the medical image. Fuzzy set theory and information theory have a huge impact on image segmentation [1][2][3]. Fuzzy entropy has become one of the important research points for threshold based medical image segmentation.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Multi-Threshold Image Segmentation of Low Contrast Medical Images

Elaraby¹,

Elansary²

2021

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Accurate medical images segmentation plays a vital role in contouring during diagnosis and treatment planning. To improve the segmentation accuracy in low contrast images, we propose a method by combining Hill entropy and fuzzy c-partition. Here, using membership function, an image is first transformed into fuzzy domain. Subsequently, the fuzzy Hill entropies are defined for foreground (object) and background. Next, the total fuzzy Hill entropy is maximized to compute the accurate threshold; this process is employed to calculate a proper parameter combination of membership function. This Hill entropy is then optimized to acquire an image threshold by Differential Evolution “DE” optimization algorithm. The key benefit of the presented approach is that it considers the information of background and object as well as interactions between them in threshold selection mechanism. The results and performance evaluations show the better accuracy of our technique over other existing approaches.

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A Modified Intuitionistic Fuzzy Clustering Algorithm for Medical Image Segmentation

Cited by 27 publications

References 38 publications

A Kernel-Based Intuitionistic Fuzzy C-Means Clustering Using a DNA Genetic Algorithm for Magnetic Resonance Image Segmentation

A Kernel-Based Intuitionistic Fuzzy C-Means Clustering Using a DNA Genetic Algorithm for Magnetic Resonance Image Segmentation

A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation

A Framework for Multi-Threshold Image Segmentation of Low Contrast Medical Images

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