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

DOI

10.2991/cnct-16.2017.82How to use a DOI?

Keywords

Micro-CT, Mouse Trabecular, LGIF Model, K-Means Cluster, Active Contour, Image Feature Analysis

Abstract

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shu-yue CHEN
AU  - Ying LI
AU  - Kai-bin CHU
PY  - 2016/12
DA  - 2016/12
TI  - Extraction and Feature Analysis of Mouse Trabecular with Active Contour Model Based on Micro-CT Images
BT  - Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
PB  - Atlantis Press
SP  - 600
EP  - 605
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnct-16.2017.82
DO  - 10.2991/cnct-16.2017.82
ID  - CHEN2016/12
ER  -