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Breast cancer is one of the most commonly diagnosed cancer types among women. Sonography has been regarded as an important imaging modality for diagnosis of breast lesions. Due to the speckle and the variance in shape and appearance of sonographic lesions, fully automatic segmentation of the breast tumor regions still remains a challenging task. In this paper, we propose an automatic interaction scheme based on an object recognition method to segment the lesions in breast ultrasound images. In this scheme, a 2D ultrasound image is firstly filtered with a total-variation model to reduce the speckle noise. A robust graph-based segmentation method is then used to segment the image into a number of sub-regions. An object recognition method incorporating the procedures of image feature extraction, feature selection and classification is proposed to automatically identify the regions which are associated with breast tumors. Finally, an active contour model is used to refine the contours of the regions that are recognized as tumors. This scheme is validated on a database of 46 breast ultrasound images with diagnosed tumors. The experimental results show that our scheme can segment the breast ultrasound images automatically, indicating its good performance in real applications. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel re-texturing approach using intrinsic video. Our approach first indicates the regions of interest by contour-aware layer segmentation. The intrinsic video including reflectance and illumination components within the segmented region is recovered by our weighted energy optimization. We then compute the texture coordinates in key frames and the normals for the re-textured region using the optimization approach we develop. Meanwhile, the texture coordinates in non-key frames are optimized by our energy function. When the target sample texture is specified, the re-textured video is finally created by multiplying the re-textured reflectance component with the original illumination component within the replaced region. As shown in our experimental results, our method can produce high quality video re-texturing results with a variety of sample textures, and also the lighting and shading effects of the original videos are well preserved after re-texturing. [ABSTRACT FROM AUTHOR]