DNA　microarray　technology　has　enabled　biologists　to　study　all　the　genes　within　an　entire　organism　to　obtain　a　global　view　of　genes＇　interaction　and　regulation.　This　technology　has　a　great　potential　in　obtaining　a　deep　understanding　of　the　functional　organization　of　cells.　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.　Rather　than　using　the　raw　microarray　image,　it　proposes　to　produce　filtered　versions　of　the　image　data　by　applying　nonlinear　anisotropic　diffusion　so　that　the　dynamic　range　of　the　image　could　be　increased　and,　therefore,　a　better　ability　of　signal　extraction　could　be　achieved.　In　this　paper,　a　novel　segmentation　algorithm　is　developed.　This　algorithm　is　based　on　the　Cellular　Neural　Network　computational　paradigm　integrated　with　median　and　anisotropic　diffusion　filters.　The　AnaLogic　CNN　Simulation　Toolbox　for　MATLAB　(InstantVision　Toolboxes　for　MATLAB)　is　used　during　the　segmentation　process.　Quantitative　comparisons　among　the　proposed　methods　and　GenePix®　are　carried　out　from　both　the　objective　and　subjective　points　of　view.　It　is　shown　that　analogic　algorithm　integrated　with　Complex　Diffusion　filter　is　the　best　one　to　be　applied　to　accomplish　the　segmentation.　©　2014　Elsevier　B.V.