Comparative analysis of filtering methods in fuzzy C-means: Environment for DICOM image segmentation

2021 ◽  
pp. 81-98
Author(s):  
D. Nagarajan ◽  
Kavikumar Jacob ◽  
Aida Mustapha ◽  
Udaya Mouni Boppana ◽  
Najihah Chaini
Keyword(s):  
Image Segmentation ◽  
Comparative Analysis ◽  
Dicom Image ◽  
Fuzzy C Means

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

scholarly journals A NOVEL IMAGE SEGMENTATION APPROACH BASED ON NEUTROSOPHIC SET AND IMPROVED FUZZY C-MEANS ALGORITHM

2011 ◽  
Vol 07 (01) ◽  
pp. 155-171 ◽  
Author(s):  
H. D. CHENG ◽  
YANHUI GUO ◽  
YINGTAO ZHANG
Keyword(s):  
Image Segmentation ◽  
Point Of View ◽  
Neutrosophic Set ◽  
Convergence Criterion ◽  
Complex Objects ◽  
Fuzzy C Means ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Segmentation Approach ◽  
Fuzzy C Means Algorithm

Image segmentation is an important component in image processing, pattern recognition and computer vision. Many segmentation algorithms have been proposed. However, segmentation methods for both noisy and noise-free images have not been studied in much detail. Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interaction with different ideational spectra. However, neutrosophic set needs to be specified and clarified from a technical point of view for a given application or field to demonstrate its usefulness. In this paper, we apply neutrosophic set and define some operations. Neutrosphic set is integrated with an improved fuzzy c-means method and employed for image segmentation. A new operation, α-mean operation, is proposed to reduce the set indeterminacy. An improved fuzzy c-means (IFCM) is proposed based on neutrosophic set. The computation of membership and the convergence criterion of clustering are redefined accordingly. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment images accurately and effectively. Especially, it can segment the clean images and the images having different gray levels and complex objects, which is the most difficult task for image segmentation.

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