The segmentation of images into regions that have some common properties is a fundamental problem in low level computer vision. In this paper, the region growing method to segmentation is studied. In the study, a coarse to fine processing strategy is adopted to identify the homogeneity of the subregion of an image. The pixels in the image are checked by a nested triple-layer neighborhood system based hypothesis test. The pixels can then be classified into single pixels or grain pixels with different size and coarseness. Instead of using the global threshold to the region growing, local thresholds are determined adaptively for each pixel in the image. The strength of the proposed method lies in the fact that the thresholds are computed automatically. Experiments for synthetic and natural images show the efficiency of our method.

A two-cascaded image processing approach to enhance the subtle differences in X-ray CT image is proposed. In the method, an asymmetrical non-linear subfilter is introduced to reduce the noise inherent in the image while preserving local edges and directional structural information. Then, a subfilter is used to compress the global dynamic range of the image and emphasize the details in the homogeneous regions by performing a modular transformation on local image den-sities. The modular transformation is based on a dynamically defined contrast fator and the histogram distributions of the image. The local contrast factor is described in accordance with Weber's fraction by a two-layer neighborhood system where the relative variances of the medians for eight directions are computed. This method is suitable for low contrast images with wide dynamic ranges. Experiments on X-ray CT images of the head show the validity of the method.

Voxel representation of solid object is a well known model that is widely used in CAD, robots, computer graphics, and medical image processing. Efficient algorithms for the tracing in voxel model are of great importance. A fast hardware oriented voxel tracing algorithm (called LCDDA) is proposed and implemented. All the voxels pierced by a given ray are generated incrementaly. To meve from a voxel to the next one only three fixed point additions and comparisons, which can be done using 3 parallel pipelines, and a ROM access are required. It takes 0.2 microseconds to do this under 5 MHz clock on a personal computer, which is 103 times faster than Fujimoto's and 1339 times than Glassner's algorithm on a workstation. As an application of this, time consuming ray tracing process is performed by this hardware implemented algorithm on a personal computer. Also, an active intersection table (AIT) is introduced to avoid multiple intersection of a ray and the same object. The high speed of these methods have been proved by experimental results.