| Περίληψη: | In this thesis, a comparative evaluation of five different wavelet-based filtering techniques in the task of microarray image denoising and enhancement, as well as, a new methodology for the segmentation of microarray images is developed. Clinical material comprised complementary DNA (cDNA) microarray images collected from the Oak Ridge National Laboratory, simulated data produced by using a Microarray Scan Simulator, and a set of two simulated images, each containing 200 spots. Image pre-processing was performed in two stages: In the first stage an Exponential Histogram Equalization filter was applied to real cDNA images in order to increase the contrast between spots and surrounding background. In the second stage, five wavelet-based image filters (Simple Piece-Wise Linear Mapping Filter (SPWLMF), Hard Threshold filter (HTF), Wavelet Enhancement with Noise Suppression filter (WEWNSF), Non Linear Enhancement filter (NLEF) and Sigmoidal Non-linear Enhancement filter (SNLEF)) were implemented for denoising and enhancing gene microarray spots. The enhancing effectiveness of the five filters was assessed by calculating the Mean-Square-Error (MSE) and the Signal-to-MSE ratio. An automatic gridding scheme was applied to both real and simulated cDNA images, for the task of determining spots and their borders (cells). Firstly, the segmentation capability of the Gaussian Mixture Models GMM boosted by the five wavelet based preprocessing filters was evaluated by calculating the segmentation matching factor for each spot. Significant noise suppression was accomplished by the SPWLMP filter, which scored the minimum MSE and the maximum Signal-to-MSE ratio. Optimal segmentation results were obtained by pre-processing the microarray image by all the wavelet-based filters. Finally, a new methodology for spot identification based on the combination of GMM clustering technique with Gradient Vector Flow (GVF) active contours was introduced. According to that method, a GMM clustering algorithm was firstly applied in all individual spot images of the cDNA image. Afterwards, the output of the GMM algorithm was used to utilize a Gradient Vector Flow (GVF) active contour. The major advance of our method is that it overcomes limitations of GMM and deformable models when used individually. For the evaluation of our method, segmentation matching factors, as well as mean intensity value were calculated for every cell using GMM, GVF active contours and GMM and GVF active contours combination. Numerical experiments using simulated cDNA images have also shown that our method was more accurate in measuring mean intensity values and detecting real boundaries of spots with foreground mean intensity value close to the background, compared with GMM and snakes used individually.
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