We have developed an active shape model (ASM)-based segmentation scheme that uses the original Cootes et al. formulation for the underlying mechanics of the ASM but improves the model by fixating selected nodes at specific structural boundaries called transitional landmarks. Transitional landmarks identify the change from one boundary type (such as lung-field/heart) to another (lung-field/diaphragm). This results in a multi-segmented lung-field boundary where each segment correlates to a specific boundary type (lung-field/heart, lung-field/aorta, lung-field/rib-cage, etc.). The node-specified ASM is built using a fixed set of equally spaced feature nodes for each boundary segment. This allows the nodes to learn local appearance models for a specific boundary type, rather than generalizing over multiple boundary types, which results in a marked improvement in boundary accuracy. In contrast, existing lung-field segmentation algorithms based only on ASM simply space the nodes equally along the entire boundary without specification. We have performed extensive experiments using multiple datasets (public and private) and compared the performance of the proposed scheme with other contour-based methods. Overall, the improved accuracy is 3-5 &percent; over the standard ASM and, more importantly, it corresponds to increased alignment with salient anatomical structures. Furthermore, the automatically generated lung-field masks lead to the same fROC for lung-nodule detection as hand-drawn lung-field masks. The accurate landmarks can be easily used for detecting other structures in the lung field. Based on the related landmarks (mediastinum-heart transition, heart-diaphragm transition), we have extended the work to heart segmentation.
Chest radiography is one of the most widely used techniques in diagnostic imaging. It makes up at least one third of all conventional diagnostic radiographic procedures in hospitals. However, in both film-screen and computed radiography, images are often digitized with the view and orientation unknown or mislabeled, which causes inefficiency in displaying them in the picture archive and communication system (PACS). Hence, the goal of this work is to provide a robust, efficient, and automatic hanging protocol for chest radiographs. To achieve it, the method star ts with recognition by extracting a set of distinctive features from chest radiographs. Next, a well-defined probabilistic classifier is used to train and classify the radiographs. Identifying the orientation of the radiographs is performed by an efficient algorithm which locates the neck, heart, and abdomen positions in radiographs. The initial experiment was performed on radiographs collected from daily routine chest exams in hospitals, and it has shown promising results.