In this paper, we propose a new deformable model a robust snake model, which solves the primary problems suffered by the conventional snake, such as contour initialization, proper internal parameter setting and the limited capture range of the external energy. A reformulated internal energy is used to serve the smoothness of snake contour without a contraction of the contour. The external energy combines both region and edge information to enlarge the capture range, and also reduces the requirement of initial contour. Both synthetic and real gray-level images are selected to evaluate the performance of the proposed model. Its implementation show it robust, fast and accurate. Initial experimental results are encouraging.

In this paper, a knowledge-based, fully automatic method for identifying lung regions in digital chest radiographs is described. The method uses an object-oriented knowledge model to appropriately integrate the anatomical knowledge and image processing routines in lung detection. A series of chest radiographs are employed to test the proposed method; the experimental results are encouraging.

Proposes an image enhancement algorithm that converts raw data from a storage phosphor scanner into a diagnostically optimized image. The spatial segmentation algorithm is applied to extract the body part of a clinical image, and histogram analysis is then used to develop the tone-scale for the region of interest. Further enhancement is obtained by unsharp masking. This approach reduces the sensitivity to nonanatomical regions and allows full use of the display's dynamic range for image viewing. The algorithms have been tested on clinical images and the results were reviewed by radiologists. Currently, these algorithms are being successfully used in the clinical environment.>