Multiscale superpixel classification for tumor segmentation in breast ultrasound images

Tumor localization and segmentation in breast ultrasound (BUS) images is an important as well as intractable problem for computer-aided diagnosis (CAD) due to the high variation in shape and appearance. We propose a novel algorithm in this paper without making any assumption on tumor, compared to most previous works. Heterogeneous features are collected via a hierarchical over-segmentation framework, which we have shown has the multiscale property. The superpixels are then classified with their confidences nested into the bottom layer. The ultimate segmentation is made by using an efficient conditional random field model. Experiments on challenging data set show that our algorithm is able to handle almost all kinds of benign and malignant tumors, and also confirm the superiority of our work through a comparison with other two different approaches.

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