scholarly journals Fuzzy C-Means Clustering Based on Improved Marked Watershed Transformation

2016 ◽  
Vol 14 (3) ◽  
pp. 981 ◽  
Author(s):  
Cuijie Zhao ◽  
Hongdong Zhao ◽  
Wei Yao
Keyword(s):  
Watershed Transformation ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

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.

Segmentation of Tuberculosis Bacilli Using Watershed Transformation and Fuzzy C-Means

2019 ◽  
Vol 13 (1) ◽  
pp. 9
Author(s):  
Rahadian Kurniawan ◽  
Izzati Muhimmah ◽  
Arrie Kurniawardhani ◽  
Sri Kusumadewi
Keyword(s):  
Microscopic Analysis ◽  
High Accuracy ◽  
Kappa Coefficient ◽  
Field Of View ◽  
Resistant Bacteria ◽  
Screening Process ◽  
Watershed Transformation ◽  
Fuzzy C Means ◽  
Primary Health ◽  
Manual Calculation

The easily transmitted Tuberculosis (TB) disease is attributed to the fact that Mycobacterium Tuberculosis (MTB) bacteria/viruses can be transmitted through the air. One of the methods to screen the TB disease is by reading sputum slides. Sputum slides are colored sputum samples of TB patients placed on microscopic slides. However, TB disease microscopic analysis has some limitations since it requires high accuracy reading and well-trained health personnel to avoid errors in the process of interpretation. Furthermore, the number of TB patients in the Primary Health Care (PHC) and the process of manual calculation of bacteria in a field of view often complicate the decision-making in the screening process conducted by the medical staffs. In this paper, the researchers propose the use of Watershed Transformation and Fuzzy C-Means combination to help solve the problem. The researchers collect the photo shooting of three PHC in Indonesia with 55 images of sputum from different TB patients. The assessed results of the proposed method are compared with the opinions of three Microbiology doctors. The comparison shows Cohen’s Kappa Coefficient value of 0.838. It suggests that the proposed method can detect Acid Resistant Bacteria (ARB) although it needs some improvement to achieve higher accuracy.

scholarly journals A Novel Coarse-to-fine Sea-land Segmentation Technique Based on Superpixel Fuzzy C-Means Clustering and Modified Chan-Vese Model

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Eman Elkhateeb ◽  
Hassan Soliman ◽  
Ahmed Atwan ◽  
Mohammed Elmogy ◽  
Kyung-Sup Kwak ◽  
...  
Keyword(s):  
Fuzzy C Means ◽  
Segmentation Technique ◽  
Fuzzy C Means Clustering ◽  
Coarse To Fine
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