DNA Microarray Images: Processing, Modelling, Compression

Abstract

DNA Microarray is an innovative tool for gene studies in biomedical research. It is capable of testing and extracting the expression of large number of genes in parallel. Its applications can vary from cancer diagnosis to human identification. A DNA microarray experiment generates an image which has the genetic data embedded in it. Fast, accurate, and automatic routines for processing and compression of these images do not exist. For processing and modelling of micoarray images, we introduce a new, fast and accurate approach in this thesis. A new lossless compression method for microarray images is introduced that provides an average compression ratio of 1.89:1, and that outperforms other lossless compression schemes and the work of other researchers in this field. For the lossy compression, our new method has overcome the rate-distortion curve of JPEG. A new scanning method called spiral path, and a new spatial transform called C2S are introduced in this thesis for lossless and lossy compression of microarray images.