Segmentation of MRI data using multi-objective antlion based improved fuzzy c-means

Singh, Munendra; Venkatesh, Vishal; Verma, Ashish; Sharma, Neeraj

doi:10.1016/j.bbe.2020.07.001

Cited by 12 publications

(5 citation statements)

References 42 publications

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“…The 2D images of 89 cross sections (89 CS), 92 cross sections (95 CS), and 95 cross sections (95 CS) in the T1-weighted brain MRI image were selected for segmentation. The comparison algorithms used were FCM, CoFKM (Cai et al, 2019), two-layer automatic weighted clustering algorithm (TW-k-means) (Singh et al, 2020), multitask-based K-means (CombKM) (Chen et al, 2013), and collaborative clustering based on sample and feature space (coclustering) (Gu and Zhou, 2009). In the experiment, the iteration stop threshold ε of each algorithm is set to 0.001, and the maximum number of iterations l was set to 100.…”

Section: Simulation Experiments Analysis Experimental Backgroundmentioning

confidence: 99%

A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View Fuzzy c-Means Clustering Algorithm

Lee

et al. 2021

Front. Neurosci.

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Background: The brain magnetic resonance imaging (MRI) image segmentation method mainly refers to the division of brain tissue, which can be divided into tissue parts such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The segmentation results can provide a basis for medical image registration, 3D reconstruction, and visualization. Generally, MRI images have defects such as partial volume effects, uneven grayscale, and noise. Therefore, in practical applications, the segmentation of brain MRI images has difficulty obtaining high accuracy.Materials and Methods: The fuzzy clustering algorithm establishes the expression of the uncertainty of the sample category and can describe the ambiguity brought by the partial volume effect to the brain MRI image, so it is very suitable for brain MRI image segmentation (B-MRI-IS). The classic fuzzy c-means (FCM) algorithm is extremely sensitive to noise and offset fields. If the algorithm is used directly to segment the brain MRI image, the ideal segmentation result cannot be obtained. Accordingly, considering the defects of MRI medical images, this study uses an improved multiview FCM clustering algorithm (IMV-FCM) to improve the algorithm’s segmentation accuracy of brain images. IMV-FCM uses a view weight adaptive learning mechanism so that each view obtains the optimal weight according to its cluster contribution. The final division result is obtained through the view ensemble method. Under the view weight adaptive learning mechanism, the coordination between various views is more flexible, and each view can be adaptively learned to achieve better clustering effects.Results: The segmentation results of a large number of brain MRI images show that IMV-FCM has better segmentation performance and can accurately segment brain tissue. Compared with several related clustering algorithms, the IMV-FCM algorithm has better adaptability and better clustering performance.

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Section: Simulation Experiments Analysis Experimental Backgroundmentioning

confidence: 99%

A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View Fuzzy c-Means Clustering Algorithm

Lee

et al. 2021

Front. Neurosci.

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“…This classifier determines the kind of tumor. When the linear kernel function of three SVM classifier kernel functions is compared, it yields a more accurate result [ 6 ]. In 2021, Sangeeta et al reported on an effective image divisions approach based on K-implies bunching.…”

Section: Literature Reviewmentioning

confidence: 99%

Modified Artificial Bee Colony Algorithm-Based Strategy for Brain Tumor Segmentation

Dahiya

Kumar

et al. 2022

Computational Intelligence and Neuroscience

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Medical image segmentation is a technique for detecting boundaries in a 2D or 3D image automatically or semiautomatically. The enormous range of the medical image is a considerable challenge for image segmentation. Magnetic resonance imaging (MRI) scans to aid in the detection and existence of brain tumors. This approach, however, requires exact delineation of the tumor location inside the brain scan. To solve this, an optimization algorithm will be one of the most successful techniques for distinguishing pixels of interest from the background, but its performance is reliant on the starting values of the centroids. The primary goal of this work is to segment tumor areas within brain MRI images. After converting the gray MRI image to a color image, a multiobjective modified ABC algorithm is utilized to separate the tumor from the brain. The intensity determines the RGB color generated in the image. The simulation results are assessed in terms of performance metrics such as accuracy, precision, specificity, recall, F-measure, and the time in seconds required by the system to segment the tumor from the brain. The performance of the proposed algorithm is computed with other algorithms like the single-objective ABC algorithm and multiobjective ABC algorithm. The results prove that the proposed multiobjective modified ABC algorithm is efficient in analyzing and segmenting the tumor from brain images.

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“…In this context, many variants exist, either those based on a similarity classification in a Euclidean-metric space (e.g., K-means [ 26 ], fuzzy c-means [ 27 , 28 ]), or those using probabilistic modelling (e.g., Gaussian mixture model [ 29 ]) which assume the presence of different datasets. In the latter, each Gaussian component is characterized with a probability distribution function which needs to be determined, say, from their maximum likelihood.…”

Section: Introductionmentioning

confidence: 99%

PeakForce AFM Analysis Enhanced with Model Reduction Techniques

Chang

Hallais

Danas

et al. 2023

Sensors

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PeakForce quantitative nanomechanical AFM mode (PF-QNM) is a popular AFM technique designed to measure multiple mechanical features (e.g., adhesion, apparent modulus, etc.) simultaneously at the exact same spatial coordinates with a robust scanning frequency. This paper proposes compressing the initial high-dimensional dataset obtained from the PeakForce AFM mode into a subset of much lower dimensionality by a sequence of proper orthogonal decomposition (POD) reduction and subsequent machine learning on the low-dimensionality data. A substantial reduction in user dependency and subjectivity of the extracted results is obtained. The underlying parameters, or “state variables”, governing the mechanical response can be easily extracted from the latter using various machine learning techniques. Two samples are investigated to illustrate the proposed procedure (i) a polystyrene film with low-density polyethylene nano-pods and (ii) a PDMS film with carbon–iron particles. The heterogeneity of material, as well as the sharp variation in topography, make the segmentation challenging. Nonetheless, the underlying parameters describing the mechanical response naturally offer a compact representation allowing for a more straightforward interpretation of the high-dimensional force–indentation data in terms of the nature (and proportion) of phases, interfaces, or topography. Finally, those techniques come with a low processing time cost and do not require a prior mechanical model.

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Segmentation of MRI data using multi-objective antlion based improved fuzzy c-means

Cited by 12 publications

References 42 publications

A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View Fuzzy c-Means Clustering Algorithm

A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View Fuzzy c-Means Clustering Algorithm

Modified Artificial Bee Colony Algorithm-Based Strategy for Brain Tumor Segmentation

PeakForce AFM Analysis Enhanced with Model Reduction Techniques

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