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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

Results of the mathematical morphology in the gridding of breast cancer microarray image. (a) Microarray image includes the red and green channels. (b and c) Extraction of the red and green channels in the microarray image. (d) Extraction of the one-dimensional vertical projection signal. (e) Reconstruction of the vertical projection of signal using the mathematical morphology operations. (f) Drawing of the horizontal lines. (g) Final gridded image
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Figure 3: Results of the mathematical morphology in the gridding of breast cancer microarray image. (a) Microarray image includes the red and green channels. (b and c) Extraction of the red and green channels in the microarray image. (d) Extraction of the one-dimensional vertical projection signal. (e) Reconstruction of the vertical projection of signal using the mathematical morphology operations. (f) Drawing of the horizontal lines. (g) Final gridded image

Mentions: In Figure 3a, a sample of microarray image has been shown, which is related to a patient suffered from breast cancer. Figure 3b and e are the red and green channels extracted from the original image, respectively. The results of horizontal projection by applying mathematical morphology operations and also the gridded network of the microarray image have been shown in Figure 3d-f, respectively. As can be seen in Figure 3g, the coordinate of each spot in every sub-network is calculated very well.


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)

Results of the mathematical morphology in the gridding of breast cancer microarray image. (a) Microarray image includes the red and green channels. (b and c) Extraction of the red and green channels in the microarray image. (d) Extraction of the one-dimensional vertical projection signal. (e) Reconstruction of the vertical projection of signal using the mathematical morphology operations. (f) Drawing of the horizontal lines. (g) Final gridded image
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Results of the mathematical morphology in the gridding of breast cancer microarray image. (a) Microarray image includes the red and green channels. (b and c) Extraction of the red and green channels in the microarray image. (d) Extraction of the one-dimensional vertical projection signal. (e) Reconstruction of the vertical projection of signal using the mathematical morphology operations. (f) Drawing of the horizontal lines. (g) Final gridded image
Mentions: In Figure 3a, a sample of microarray image has been shown, which is related to a patient suffered from breast cancer. Figure 3b and e are the red and green channels extracted from the original image, respectively. The results of horizontal projection by applying mathematical morphology operations and also the gridded network of the microarray image have been shown in Figure 3d-f, respectively. As can be seen in Figure 3g, the coordinate of each spot in every sub-network is calculated very well.

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