Fully Automated Complementary DNA Microarray Segmentation using a Novel Fuzzy-based Algorithm.
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.
Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
Hamidreza
H
Saberkari
Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
Sheyda
S
Bahrami
Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
Mousa
M
Shamsi
Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
Mohammad Javad
MJ
Amoshahy
Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
Habib Badri
HB
Ghavifekr
Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
Mohammad Hossein
MH
Sedaaghi
Breast cancer; fuzzy clustering; gene expression; microarray; noise