Mendhurwar Kaustubha A, Kakumani Rajasekhar, Devabhaktuni Vijay
Faculty of Engineering and Computer Science, Concordia University, 1455 de Maisonneuve Blvd. West, Montreal, H3G1M8, Quebec, Canada.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3629-32. doi: 10.1109/IEMBS.2009.5333761.
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.
微阵列技术被认为是生物信息学领域的重大突破之一，它能够同时对数千个基因的表达进行分析。微阵列图像分析在准确描述基因表达方面起着重要作用。微阵列图像的分割，即将前景与背景分离的过程，是微阵列图像分析中的关键问题之一。水平集在图像分割方面具有巨大潜力。本文提出了一种微阵列图像分割的新方法。在这项工作中，采用了Mumford-Shah模型的Chan-Vese近似和水平集方法进行图像分割。给出了该方法的示例，突出了其有效性。
