Limits...
Fully Automated Complementary DNA Microarray Segmentation using a Novel Fuzzy-based Algorithm.

Saberkari H, Bahrami S, Shamsi M, Amoshahy MJ, Ghavifekr HB, Sedaaghi MH - J Med Signals Sens (2015 Jul-Sep)

Bottom Line: Using a Gaussian kernel in SFCM algorithm will lead to improving the quality in complementary DNA microarray segmentation.The performance of the proposed algorithm has been evaluated on the real microarray images, which is available in Stanford Microarray Databases.Results illustrate that the accuracy of microarray cells segmentation in the proposed algorithm reaches to 100% and 98% for noiseless/noisy cells, respectively.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.

ABSTRACT
DNA microarray is a powerful approach to study simultaneously, the expression of 1000 of genes in a single experiment. The average value of the fluorescent intensity could be calculated in a microarray experiment. The calculated intensity values are very close in amount to the levels of expression of a particular gene. However, determining the appropriate position of every spot in microarray images is a main challenge, which leads to the accurate classification of normal and abnormal (cancer) cells. In this paper, first a preprocessing approach is performed to eliminate the noise and artifacts available in microarray cells using the nonlinear anisotropic diffusion filtering method. Then, the coordinate center of each spot is positioned utilizing the mathematical morphology operations. Finally, the position of each spot is exactly determined through applying a novel hybrid model based on the principle component analysis and the spatial fuzzy c-means clustering (SFCM) algorithm. Using a Gaussian kernel in SFCM algorithm will lead to improving the quality in complementary DNA microarray segmentation. The performance of the proposed algorithm has been evaluated on the real microarray images, which is available in Stanford Microarray Databases. Results illustrate that the accuracy of microarray cells segmentation in the proposed algorithm reaches to 100% and 98% for noiseless/noisy cells, respectively.

No MeSH data available.


Related in: MedlinePlus

Proposed algorithm of the spatial fuzzy clustering for microarray cell segmentation
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4528357&req=5

Figure 4: Proposed algorithm of the spatial fuzzy clustering for microarray cell segmentation

Mentions: where, P and q are the control parameter of each two function. In the first step (similar to fuzzy clustering algorithm), the membership function is calculated. In the second step, the information of membership function is mapped to the spatial area, and then the spatial function is calculated. It should be noticed that in the homogeneous region, the spatial functions are similar to the main membership function. As a result, there is no important variation in the clustering results. However, for the noisy pixels, the above relation reduces the weight of the noisy clusters by labeling the neighbor pixels. Consequently, the wrong classified pixels were easily modified from noisy areas. The block diagram of the spatial fuzzy clustering algorithm is shown in Figure 4.


Fully Automated Complementary DNA Microarray Segmentation using a Novel Fuzzy-based Algorithm.

Saberkari H, Bahrami S, Shamsi M, Amoshahy MJ, Ghavifekr HB, Sedaaghi MH - J Med Signals Sens (2015 Jul-Sep)

Proposed algorithm of the spatial fuzzy clustering for microarray cell segmentation
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4528357&req=5

Figure 4: Proposed algorithm of the spatial fuzzy clustering for microarray cell segmentation
Mentions: where, P and q are the control parameter of each two function. In the first step (similar to fuzzy clustering algorithm), the membership function is calculated. In the second step, the information of membership function is mapped to the spatial area, and then the spatial function is calculated. It should be noticed that in the homogeneous region, the spatial functions are similar to the main membership function. As a result, there is no important variation in the clustering results. However, for the noisy pixels, the above relation reduces the weight of the noisy clusters by labeling the neighbor pixels. Consequently, the wrong classified pixels were easily modified from noisy areas. The block diagram of the spatial fuzzy clustering algorithm is shown in Figure 4.

Bottom Line: Using a Gaussian kernel in SFCM algorithm will lead to improving the quality in complementary DNA microarray segmentation.The performance of the proposed algorithm has been evaluated on the real microarray images, which is available in Stanford Microarray Databases.Results illustrate that the accuracy of microarray cells segmentation in the proposed algorithm reaches to 100% and 98% for noiseless/noisy cells, respectively.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.

ABSTRACT
DNA microarray is a powerful approach to study simultaneously, the expression of 1000 of genes in a single experiment. The average value of the fluorescent intensity could be calculated in a microarray experiment. The calculated intensity values are very close in amount to the levels of expression of a particular gene. However, determining the appropriate position of every spot in microarray images is a main challenge, which leads to the accurate classification of normal and abnormal (cancer) cells. In this paper, first a preprocessing approach is performed to eliminate the noise and artifacts available in microarray cells using the nonlinear anisotropic diffusion filtering method. Then, the coordinate center of each spot is positioned utilizing the mathematical morphology operations. Finally, the position of each spot is exactly determined through applying a novel hybrid model based on the principle component analysis and the spatial fuzzy c-means clustering (SFCM) algorithm. Using a Gaussian kernel in SFCM algorithm will lead to improving the quality in complementary DNA microarray segmentation. The performance of the proposed algorithm has been evaluated on the real microarray images, which is available in Stanford Microarray Databases. Results illustrate that the accuracy of microarray cells segmentation in the proposed algorithm reaches to 100% and 98% for noiseless/noisy cells, respectively.

No MeSH data available.


Related in: MedlinePlus