KEYWORDS: Image segmentation, Medical imaging, 3D modeling, 3D image processing, Image processing, Image analysis, 3D image reconstruction, Volume rendering, Data modeling, Surgery
An integrated 3D medical image processing and analysis system we developed can provide powerful functions such as image preprocessing, virtual cutting, surface rendering, volume rendering, and manipulation. The system description, the method adopted and the application examples are presented. The system can be widely applied to processing and analysis of CT and MR images.
Homogram, or histogram based on homogeneity is employed in our algorithm. Histogram thresholding is a classical and efficient method for the segmentation of various images, especially of CT images. However, MR images are difficultly segmented via this method; as the gray levels of their pixels are too similar to distinguish. The regular histogram of a MR image is usually plain, thus the peaks and valleys of the histogram are hard to find and locate precisely. We proposed a new definition of homogeneity for which a series of sub-images are employed to compute. Therefore, both local and global information are taken in accounted. Then the image is updated with the homogeneity weighted original and average gray levels. The more homogeneous the pixel is, the closer the updated gray level is to the average. The new histogram is calculated based on the updated image. It is much steeper than the regular one. Some indiscernible peaks in the regular histogram can be recognized easily from the new histogram. Therefore a simple but agile peak-finding approach is able to determine objects to segment and corresponding thresholds exactly. Segmentation via thresholding is feasible now even in MR images. Moreover, our algorithm remains speedy even though the accuracy of segmentation advances.
In this paper, an algorithm for the semiautomatic segmentation of medical image series is proposed by combining the live wire algorithm and the active contour model. First, we use the robust anisotropic diffusion filtering to smooth the images while keeping the edges. Then we modify the traditional live wire algorithm by combining it with the watershed method. Using the improved live wire method, the accurate segmentation of one or more medical images could be obtained firstly. Based on the segmentation of previous slices, the computer will segment the nearby slices using the modified active contour model automatically. To make full use of the correlative information between contiguous slices, a gray-scale model is applied to the model to record the local region characters of the desired object, and a new functional definition of the external energy is designed. Furthermore, in order to be adaptable with the topological change of the nearby slices, affine cell image decomposition is applied to the active contour model. The experiment results show that this algorithm can recover the boundary of the desired object from a series of medical images quickly and reliably with only little user intervention.
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