title: Microarray image segmentation using Chan-Vese active contour model and level set method.
abstract: Microarray technology is considered to be one of the major breakthroughs in bioinformatics for profiling gene-expressions of thousands of genes, simultaneously. Analysis of a microarray image plays an important role in the accurate depiction of gene-expression. Segmentation, the process of separating the foreground from the background, of a microarray image, is one of the key issues in microarray image analysis. Level sets have tremendous potential in the segmentation of images. In this paper, a new approach for segmentation of the microarray images is proposed. In this work, Chan-Vese approximation of the Mumford-Shah model and the level set method are employed for image segmentation. Illustrative examples of the proposed method are presented highlighting its effectiveness.
authors: Faculty of Engineering and Computer Science, Concordia University, 1455 de Maisonneuve Blvd. West, Montreal, H3G1M8, Quebec, Canada.
authors: Kaustubha A
authors: KA
authors: Mendhurwar
authors: Rajasekhar
authors: R
authors: Kakumani
authors: Vijay
authors: V
authors: Devabhaktuni
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D018076:DNA, Complementary / Q000378:metabolism; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000295:instrumentation* / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D012333:RNA, Messenger / Q000378:metabolism
chemical_list: D018076:DNA, Complementary; D012333:RNA, Messenger