Multi-scale local statistic active contour model (LSACM) level set image segmentation method
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A technology of active contour model and local statistics, applied in the fields of image processing, computer vision, and medicine, it can solve the problem that it is not suitable for segmentation of uneven grayscale images, and achieve the effect of accurate segmentation effect.
Inactive Publication Date: 2017-03-22
HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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However, the existing level set method, including the LSACM level set involved in the present invention, usually assumes that the gray scale of the image in a small local area is approximately uniform, and for the convenience of processing, it is generally uniformly predetermined in a local area. Scale, this assumption can achieve good results for the segmentation of ordinary grayscale uneven images, but it is not suitable for segmentation of severe grayscale uneven images
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Embodiment 1
[0027] see Figure 1-Figure 3 ,in figure 1 Be the flowchart of the inventive method, figure 2MR imaging from medical imaging, slice1-slice4 in (a), (b), (c), and (d) respectively reflect slices of liver images of different modalities, and the tumor appears as a white area on the liver tissue , the present invention provides a technical solution for segmenting liver tumors by using the multi-scale LSACM level set method. In this solution, the preferred constant v is selected as 0.001*255*255, and the number of iterations Ite is selected as 40. image 3 (a), (b), (c), (d) correspond to figure 2 (a), (b), (c), (d) tumor segmentation results.
[0028] The energy function of the multi-scale local statistical active contour model (LSACM) level set image segmentation method is defined as follows:
[0029]
[0030] where ∫ Ω m in H(φ)dx+∫ Ω m out (1-H(φ))dx is the data item, which is divided into the internal energy of the evolution curve ∫ Ω m in H(φ)dx and evolution c...
Embodiment 2
[0040] see figure 1 , Figure 4 and Figure 5 ,in figure 1 Be the flowchart of the inventive method, Figure 4 (a) comes from MR imaging in medical imaging, which reflects the white matter and gray matter images in the brain, Figure 4 (b) is a petal image with uneven grayscale. The present invention provides a technical solution for segmenting the above-mentioned uneven grayscale image using the multi-scale LSACM level set method. In this scheme, the preferred constant v is selected as 0.00001*255*255 , the number of iterations Ite is selected as 100, Figure 5 (a) and (b) are the segmentation results of this scheme respectively.
[0041] The energy function of the multi-scale local statistical active contour model (LSACM) level set image segmentation method is defined as follows:
[0042]
[0043] where ∫ Ω m in H(φ)dx+∫ Ω m out (1-H(φ))dx is the data item, which is divided into the internal energy of the evolution curve ∫ Ω m in H(φ)dx and evolution curve ext...
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Abstract
The invention discloses a multi-scale-based local statistic active contour model (LSACM) level set image segmentation method. The offset field B epsilon, a variance sigma i epsilon and a level set function Phi(x) in an LSACM level set method are initialized. The quantity L(x) for describing a local area characteristic in a multi-scale LSACM method is calculated. A differential characteristic d(X) which describes a multi-scale local area is calculated. The maximum response M of a high pass filter in the multi-scale LSACM method is calculated. A local area simulation gray Ci epsilon is updated. The offset field B epsilon is updated. The variance sigma i epsilon is updated. The purpose of curve evolution is achieved through solving the partial differential equation minimum value corresponding to a multi-scale LSACM level set energy function. If a set number of iterations is achieved, the iteration operation is stopped, the curve evolution is ended, and if the number of iterations is not achieved, the iteration is continued. The invention provides the multi-scale LSACM level set method, a gray uneven image can be effectively segmented, and the phenomena of excessive segmentation and insufficient segmentation in an image segmentation method are improved.
Description
technical field [0001] The invention relates to the fields of medicine, computer vision, image processing and the like, in particular to an image segmentation method based on a multi-scale local statistical active contour model (LSACM) level set. Background technique [0002] Image segmentation has always been a research hotspot in the field of computer vision, including medical image segmentation. For example, the liver is the largest solid organ in the abdominal cavity of the human body. There are many types of diseases and high incidence. In various medical imaging methods, CT imaging and MR imaging can reflect pathological morphological manifestations, but these images have a large amount of data, low contrast, and the gray level of the image is close to the surrounding tissue, and the border is blurred. The general method is not easily divisible. [0003] In recent years, level set methods have been widely used in medical image segmentation. Since the evolution curve ...
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