Group sparsity based classification for cervigram segmentation

This paper presents an algorithm to classify pixels in uterine cervix images into two classes, namely normal and abnormal tissues, and simultaneously select relevant features, using group sparsity. Because of the large variations in image appearance due to changes of illumination, specular reflections and other visual noise, the two classes have a strong overlap in feature space, whether features are obtained from color or texture information. Using more features makes the classes more separable and increases the segmentation’s quality, but also its complexity. However, the properties of these features have not been well investigated. In most cases, a group of features is selected prior to the segmentation process; features with minor contributions to the results are kept and add to the computational cost. We propose feature selection as a significant improvement in this problem. It provides a robust trade-off between segmentation quality and computational complexity. In this work...

• Type: Journal
• Year: 2011
• Where: ISBI
• Authors: Yang Yu, Junzhou Huang, Shaoting Zhang, Christophe Restif, Xiaolei Huang, Dimitris N. Metaxas
• Keywords: Abnormal Tissues, ISBI 2011, Manual Segmentation, Medical Imaging, Segmentation Problem