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The endocardium tracking in ultrasound images is challenging due to large shape variations and the signal dropout. In this paper, we present a method to fuse multiple information sources to robustly track the endocardium. The first novelty of the method is to perform tracking in a straightened shape space, to minimize the image pattern changes caused by cardiac motions. Straightened images are used...
In this paper, we propose a novel framework for detecting multiple objects in 2D and 3D images. Since a joint multi-object model is difficult to obtain in most practical situations, we focus here on detecting the objects sequentially, one-by-one. The interdependence of object poses and strong prior information embedded in our domain of medical images results in better performance than detecting the...
Lymph nodes have high clinical relevance but detection is challenging as they are hard to see due to low contrast and irregular shape. In this paper, a method for fully automatic mediastinal lymph node detection in 3-D computed tomography (CT) images of the chest area is proposed. Discriminative learning is used to detect lymph nodes based on their appearance. Because lymph nodes can easily be confused...
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