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To solve the non-uniformity of micro-CT image with CV(Chan-Vese) model and the influence of location of initial contour curves on segmentation speed in the LGIF(Local and Global Intensity Fitting) model, K-LGIF(K-means-Local and Global Intensity Fitting) model was proposed through adding K-means clustering information into energy function of LGIF active contour model. The K-LGIF model extracts outline of the image as the initial contour to reduce the number of iterations and shorten time consuming. Comparing measured geometry parameters by simulating symptoms of osteoporosis and normal mouse femur of trabecular bone and using gray level co-occurrence matrix, we measured the parameters of texture distribution of trabecular bone. The experimental results show that the K-LGIF model can effectively improve segmentation of non-uniform gray image and increase speed of segmentation. This method may provide an approach for the quantitative analysis of osteoporosis. Introduction Micro-CT (micro computed tomography) is called microscopic CT[1]. This technology has the ability to scan for live animals or a variety of hard tissue and relevant soft tissue in the body and analyze the 3D model platform. It is of good "microscopic" effect. The advantage of this technology is that without destroying the sample to learn more about the microstructure and characteristic parameters of the internal sample. Mumford and Shah functional model[2] takes advantage of energy minimization process to solve image segmentation problem. But because the local into minimum value is the one big drawback of this model, it is more difficult in the solution process. Chan and Vese simplified Mumford Shah model through implicit established a piecewise constant CV model[3] of two phase image segmentation. The model can be divided by the image as a background and target, which used the differences from the average gray value of target and background and changing automatically the topology of the curve in the evolution process for image segmentation. This method can effectively segment the uniform strength and little noise image. But at the same time, the uneven gray scale of medical images in image segmentation using CV model is not obvious. Li Chunming[4-5] who proposed to refer to the point of the Gaussian kernel function, and the function as a local binary kernel function to fit the energy of local binary fitting model (LBF), solved the shortcomings of the CV model that contains the image of local information. Li Wang put forward LGIF model which combined with the advantages of the CV model and LBF model[6]. It is global regional information and local area information geometric active contour model. Meanwhile, the low contrast image segmentation and noise robustness problem has been greatly improved with the 600 Advances in Computer Science Research (ACRS), volume 54 International Conference on Computer Networks and Communication Technology (CNCT2016)

Extraction and Feature Analysis of Mouse Trabecular with Active Contour Model Based on Micro-CT Images