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A wavelet relational fuzzy C-means algorithm for 2D gel image segmentation.

Rashwan S, Faheem MT, Sarhan A, Youssef BA - Comput Math Methods Med (2013)

Bottom Line: It has the advantages of producing high quality segmentation compared to the other available algorithms.In addition, we investigate the effect of denoising on the three algorithms.This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases) the quality of the segmentation.

View Article: PubMed Central - PubMed

Affiliation: Informatics Research Institute, City for Scientific Research and Technological Applications, Borg El Arab, Alexandria, Egypt.

ABSTRACT
One of the most famous algorithms that appeared in the area of image segmentation is the Fuzzy C-Means (FCM) algorithm. This algorithm has been used in many applications such as data analysis, pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to improve its segmentation quality. The proposed segmentation algorithm in this paper is based on the Fuzzy C-Means algorithm adding the relational fuzzy notion and the wavelet transform to it so as to enhance its performance especially in the area of 2D gel images. Both proposed modifications aim to minimize the oversegmentation error incurred by previous algorithms. The experimental results of comparing both the Fuzzy C-Means (FCM) and the Wavelet Fuzzy C-Means (WFCM) to the proposed algorithm on real 2D gel images acquired from human leukemias, HL-60 cell lines, and fetal alcohol syndrome (FAS) demonstrate the improvement achieved by the proposed algorithm in overcoming the segmentation error. In addition, we investigate the effect of denoising on the three algorithms. This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases) the quality of the segmentation.

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Related in: MedlinePlus

2D gel electrophoresis image of the first sample of patient-human leukemias: (a) original image, (b) gradient image, (c) gradient image after applying FCM segmentation algorithm, (d) gradient image after applying WFCM segmentation algorithm, and (e) gradient image after WRFCM segmentation algorithm.
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fig5: 2D gel electrophoresis image of the first sample of patient-human leukemias: (a) original image, (b) gradient image, (c) gradient image after applying FCM segmentation algorithm, (d) gradient image after applying WFCM segmentation algorithm, and (e) gradient image after WRFCM segmentation algorithm.

Mentions: The visual results of applying the conventional Fuzzy C-Means segmentation algorithm on one of these images (2D gel electrophoresis image of the first sample of Patient-Human Leukemias) at C = 2, the results of applying the Wavelet Fuzzy C-Means (WFCM) proposed, and the results of applying the proposed Wavelet Relational Fuzzy C-Means (WRFCM) algorithm at C = 4 and β = 19 are shown in Figure 5. All the implementations in this section had been performed using MATLAB 7.3.


A wavelet relational fuzzy C-means algorithm for 2D gel image segmentation.

Rashwan S, Faheem MT, Sarhan A, Youssef BA - Comput Math Methods Med (2013)

2D gel electrophoresis image of the first sample of patient-human leukemias: (a) original image, (b) gradient image, (c) gradient image after applying FCM segmentation algorithm, (d) gradient image after applying WFCM segmentation algorithm, and (e) gradient image after WRFCM segmentation algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3794507&req=5

fig5: 2D gel electrophoresis image of the first sample of patient-human leukemias: (a) original image, (b) gradient image, (c) gradient image after applying FCM segmentation algorithm, (d) gradient image after applying WFCM segmentation algorithm, and (e) gradient image after WRFCM segmentation algorithm.
Mentions: The visual results of applying the conventional Fuzzy C-Means segmentation algorithm on one of these images (2D gel electrophoresis image of the first sample of Patient-Human Leukemias) at C = 2, the results of applying the Wavelet Fuzzy C-Means (WFCM) proposed, and the results of applying the proposed Wavelet Relational Fuzzy C-Means (WRFCM) algorithm at C = 4 and β = 19 are shown in Figure 5. All the implementations in this section had been performed using MATLAB 7.3.

Bottom Line: It has the advantages of producing high quality segmentation compared to the other available algorithms.In addition, we investigate the effect of denoising on the three algorithms.This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases) the quality of the segmentation.

View Article: PubMed Central - PubMed

Affiliation: Informatics Research Institute, City for Scientific Research and Technological Applications, Borg El Arab, Alexandria, Egypt.

ABSTRACT
One of the most famous algorithms that appeared in the area of image segmentation is the Fuzzy C-Means (FCM) algorithm. This algorithm has been used in many applications such as data analysis, pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to improve its segmentation quality. The proposed segmentation algorithm in this paper is based on the Fuzzy C-Means algorithm adding the relational fuzzy notion and the wavelet transform to it so as to enhance its performance especially in the area of 2D gel images. Both proposed modifications aim to minimize the oversegmentation error incurred by previous algorithms. The experimental results of comparing both the Fuzzy C-Means (FCM) and the Wavelet Fuzzy C-Means (WFCM) to the proposed algorithm on real 2D gel images acquired from human leukemias, HL-60 cell lines, and fetal alcohol syndrome (FAS) demonstrate the improvement achieved by the proposed algorithm in overcoming the segmentation error. In addition, we investigate the effect of denoising on the three algorithms. This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases) the quality of the segmentation.

Show MeSH
Related in: MedlinePlus