Microarray　has　emerged　as　a　powerful　technology　that　enables　biologists　to　study　thousands　of　genes　simultaneously,　therefore,　to　obtain　a　better　understanding　of　the　gene　interaction　and　regulation　mechanisms.　This　paper　is　concerned　with　improving　the　processes　involved　in　the　analysis　of　microarray　image　data.　The　main　focus　is　to　clarify　an　image＇s　feature　space　in　an　unsupervised　manner.　In　this　paper,　the　Image　Transformation　Engine　(ITE),　combined　with　different　filters,　is　investigated.　The　proposed　methods　are　applied　to　a　set　of　real-world　cDNA　images.　The　MatCNN　toolbox　is　used　during　the　segmentation　process.　Quantitative　comparisons　between　different　filters　are　carried　out.　It　is　shown　that　the　CLD　filter　is　the　best　one　to　be　applied　with　the　ITE.　©　2010　Inderscience　Enterprises　Ltd.