KEYWORDS: Image segmentation, Medical imaging, Image processing algorithms and systems, Computer programming, Image processing, Data modeling, 3D modeling, Reliability, 3D image processing, Performance modeling
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. Firstly the accurate segmented results of one or more slices in a medical image series are obtained by the livewire algorithm and the watershed transform. Based on the segmentation of previous slices, the computer will segment the nearby slice using the modified active contour model automatically. To make full use of the correlative information between contiguous slices, we introduce a gray-scale model to the boundary points of the active contour model to record the local region characters of the desired object in the segmented slice and scale model. Moreover we introduce the active region concept of the snake to improve the segmentation accuracy. Experiment shows that our algorithm can obtain the boundary of the desired object from a series of medical images quickly and reliably with only little user intervention.
One of the most popular level set algorithms is the so-called fast marching method. In this paper, a medical image segmentation algorithm is proposed based on the combination of fast marching method and watershed transformation. First, the original image is smoothed using nonlinear diffusion filter, then the smoothed image is over-segmented by the watershed algorithm. Last, the image is segmented automatically using the modified fast marching method. Due to introducing over-segmentation, the arrival time the seeded point to the boundary of region should be calculated. For other pixels inside the region of the seeded point, the arrival time is not calculated because of the region homogeneity. So the algorithm’s speed improves greatly. Moreover, the speed function is redefined based on the statistical similarity degree of the nearby regions. We also extend our algorithm to 3D circumstance and segment medical image series. Experiments show that the algorithm can fast and accurately obtain segmentation results of medical images.
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.
To study the technique and application of perfusion weighted imaging (PWI) in the diagnosis and medical treatment of acute stroke, 25 patients were examined by 1.5 T or 1.0 T MRI scanner. The Data analysis was done with "3D Med System" developed by our Lab to process the data and obtain apparent diffusion coefficient (ADC) map, cerebral blood volume (CBV) map, cerebral blood flow (CBF) map as well as mean transit time (MTT) map. In accute stage of stroke, normal or slightly hypointensity in T1-, hyperintensity in T2- and diffusion-weighted images were seen in the cerebral infarction areas. There were hypointensity in CBV map, CBF map and ADC map; and hyperintensity in MTT map that means this infarct area could be saved. If the hyperintensity area in MTT map was larger than the area in diffusion weighted imaging (DWI), the larger part was called penumbra and could be cured by an appropriate thrombolyitic or other therapy. The CBV, CBF and MTT maps are very important in the diagnosis and medical treatment of acute especially hyperacute stroke. Comparing with DWI, we can easily know the situation of penumbra and the effect of curvative therapy. Besides, we can also make a differential diagnosis with this method.
One of the most popular level set algorithms is the so-called fast marching method. In this paper, a medical image segmentation method is proposed based on the combination of fast marching method and watershed transformation. First, the original image is smoothed using nonlinear diffusion filter, then the smoothed image is over-segmented by the watershed algorithm. Last, the image is segmented automatically using the modified fast marching method. Due to introducing over-segmentation, the arrival time the seeded point to the boundary of region should be calculated. For other pixels inside the region of the seeded point, the arrival time is not calculated because of the region homogeneity. So the algorithm's speed improves greatly. Moreover, the speed function is defmed again based on the statistical similarity degree of the nearby regions. Experiments show that the algorithm can fast and accurately obtain segmentation results ofmedical images.
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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