[Segmenting lung fields in serial chest radiographs using both population and patient-specific shape statistics].

This paper presents a new deformable model using both population-based and patient-specific shape statistics to segment lung fields from serial chest radiographs. First, a modified scale-invariant feature transform (SIFT) local descriptor is used to characterize the image features in the vicinity of each pixel, so that the deformable model deforms in a way that seeks for the region with similar SIFT local descriptors; second, the deformable model is constrained by both population-based and patient-specific shape statistics. At first, population-based shape statistics plays an leading role when the number of serial images is small, and gradually, patient-specific shape statistics plays a more and more important role after a sufficient number of segmentation results on the same patient have been obtained. The proposed deformable model can adapt to the shape variability of different patients, and obtain more robust and accurate segmentation results.

[Multi-target extraction algorithm based on edge restriction and attraction field regularization].

The topologically-adaptable model is an effective method for the contour detection of multiple objects on an image. However, it meets many problems when we apply it to MR brain images, such as poor convergence to boundary concavities, resulting from the broken boundary, and miserable anti-noise ability. In this paper, we proposes a new algorithm, named multi-target extraction algorithm based on edge restriction and attraction field regularization, to overcome these shortcomings. This new algorithm uses prior knowledge about target to perform edge restriction to get the only edge of the object of interest and to regularize attraction field to enlarge attraction field. Results show that the new algorithm can extract the target contour quickly and accurately when we apply it in MR brain images.