A Fuzzy C Mean Clustering Algorithm for Automated Segmentation of Brain MRI

Paul, Geenu; Varghese, Tinu; Kailasnath, Purushothaman; Singh, Natthan

doi:10.1007/978-3-319-02931-3_8

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…The unsupervised methods, such as region growing, thresholding, clustering, and statistical models, directly use the image intensity to search the object. For example, the fuzzy c -means method classifies image by grouping similar data that are present into clusters and varying the degree of membership function allows the voxel to belong to the multiple classes [ 4 , 5 ]. This assumption may not work well as it only considers intensity of image and intensity is not enough to express the intrinsic feature of objects.…”

Section: Introductionmentioning

confidence: 99%

Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder

Zhao

Wang

Niu

et al. 2016

BioMed Research International

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Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain.

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Section: Introductionmentioning

confidence: 99%

Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder

Zhao

Wang

Niu

et al. 2016

BioMed Research International

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“…Fuzzy clustering is a commonly used unsupervised learning algorithm for segmentation. Expectation maximization (EM) [4] and fuzzy c-means (FCM) algorithms [5][6][7][8] are the most popular clustering algorithms. EM algorithms have been used for segmentation of brain MRIs.…”

Section: Introductionmentioning

confidence: 99%

GPU‐based parallel fuzzy c‐mean clustering model via genetic algorithm

Hung

Wu²

2015

Concurrency and Computation

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SUMMARYDetection of white matter changes in brain tissue using magnetic resonance imaging has been an increasingly active and challenging research area in computational neuroscience. A genetic algorithm based on a fuzzy c-mean clustering method (GAFCM) was applied to simulated images to separate foreground spot signal information from the background, and the results were compared. The strength of this algorithm was tested by evaluating the segmentation matching factor, coefficient of determination, concordance correlation, and gene expression values. The experimental results demonstrated that the segmentation ability of GAFCM was better than that of fuzzy c-means and K-means algorithms. However, GAFCM is computationally expensive. This study presents a new GPU-based parallel GAFCM algorithm to improve the performance of GAFCM. The experimental results show that computational performance can be increased by a factor of approximately 20 over the CPU-based GAFCM algorithm while maintaining the quality of the processed images. Thus, the proposed GPU-based parallel GAFCM algorithm can achieve the same results and significantly decrease processing time.

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