Markov random field modeling in image analysis
Markov random field modeling in image analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy C-Means Clustering-Based Speaker Verification
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
An automatic microarray image gridding technique based on continuous wavelet transform
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
A conditional random field approach to unsupervised texture image segmentation
EURASIP Journal on Advances in Signal Processing
A wavelet-based Markov random field segmentation model in segmenting microarray experiments
Computer Methods and Programs in Biomedicine
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A wavelet-based modification of the Markov random field (WMRF) model is proposed for segmenting complementary DNA (cDNA) microarray images. For evaluation purposes, five simulated and a set of five real microarray images were used. The one-level stationary wavelet transform (SWT) of each microarray image was used to form two images, a denoised image, using hard thresholding filter, and a magnitude image, from the amplitudes of the horizontal and vertical components of SWT. Elements from these two images were suitably combined to form the WMRF model for segmenting spots from their background. The WMRF was compared against the conventional MRF and the Fuzzy C means (FCM) algorithms on simulated and real microarray images and their performances were evaluated by means of the segmentation matching factor (SMF) and the coefficient of determination (r2). Additionally, the WMRF was compared against the SPOT and SCANALYZE, and performances were evaluated by the mean absolute error (MAE) and the coefficient of variation (CV). The WMRF performed more accurately than the MRF and FCM (SMF: 92.66, 92.15, and 89.22, r2: 0.92, 0.90, and 0.84, respectively) and achieved higher reproducibility than the MRF, SPOT, and SCANALYZE (MAE: 497, 1215, 1180, and 503, CV: 0.88, 1.15, 0.93, and 0.90, respectively).