The image processing methodologies that have been actively studied and developed now play a very significant role in the flourishing biotechnology research. This work studies, develops and implements several image processing techniques for M-FISH and cDNA microarray images. In particular, we focus on three important areas: M-FISH image compression, microarray image processing and expression-based classification. Two schemes, embedded M-FISH image coding (EMIC) and Microarray BASICA: Background Adjustment, Segmentation, Image Compression and Analysis, have been introduced for M-FISH image compression and microarray image processing, respectively. In the expression-based classification area, we investigate the relationship between optimal number of features and sample size, either analytically or through simulation, for various classifiers.
The image processing methodologies that have been actively studied and developed now play a very significant role in the flourishing biotechnology research. This work studies, develops and implements several image processing techniques for M-FISH and cDNA microarray images. In particular, we focus on three important areas: M-FISH image compression, microarray image processing and expression-based classification. Two schemes, embedded M-FISH image coding (EMIC) and Microarray BASICA: Background Adjustment, Segmentation, Image Compression and Analysis, have been introduced for M-FISH image compression and microarray image processing, respectively. In the expression-based classification area, we investigate the relationship between optimal number of features and sample size, either analytically or through simulation, for various classifiers.
