Interactive CT image segmentation with online discriminative learning
Although interactive image segmentation has been widely exploited, current approaches present unsatisfactory results in medical image processing. This paper proposes a fast method for interactive CT image segmentation in which the tumor regions should be partitioned as foreground against the healthy tissues. In contrast to natural images, we have the following observation on CT images: (1) CT images often include discontinuous silhouette or cluttered spots caused by input devices or patient corporeity; (2) Disease areas often have varying appearance and shape. We thus train a discriminative foreground/background model based on user-placed scribbles. In our method, we extract positive and negative samples according to the foreground and background scribbles respectively, and use dense SIFT descriptors plus gray-level histogram as candidate features. With online learning, segmentation can be fast solved by the Bregman iteration. We test our method on CT liver images and demonstrate the advantage by comparing to state-of-the-art approaches.