The purpose of this work was to develop an automatic boundary detection method for mammographic masses and to observe the method's performance on different four of the five m...
Lisa Kinnard, Shih-Chung Ben Lo, Erini Makariou, T...
Medical volume images contain ambiguous and low-contrast boundaries around which existing fully- or semiautomatic segmentation algorithms often cause errors. In this paper, we pro...
Abstract. This paper evaluates strategies to combine multiple segmentations of the same image, generated for example by different segmentation methods or by different human experts...
Torsten Rohlfing, Daniel B. Russakoff, Calvin R. M...
Purely bottom-up, unsupervised segmentation of a single
image into two segments remains a challenging task for
computer vision. The co-segmentation problem is the process
of joi...
We address the problem of learning object class models and object segmentations from unannotated images. We introduce LOCUS (Learning Object Classes with Unsupervised Segmentation...