Improvement of Liver Segmentation by Combining High Order Statistical Texture Features with Anatomical Structural Features

DOI: 10.4236/eng.2013.55B014   PDF   HTML     2,626 Downloads   3,751 Views   Citations

Abstract

Automatic segmentation of liver in medical images is challenging on the aspects of accuracy, automation and robustness. A crucial stage of the liver segmentation is the selection of the image features for the segmentation. This paper presents an accurate liver segmentation algorithm. The approach starts with a texture analysis which results in an optimal set of texture features including high order statistical texture features and anatomical structural features. Then, it creates liver distribution image by classifying the original image pixelwisely using support vector machines. Lastly, it uses a group of morphological operations to locate the liver organ accurately in the image. The novelty of the approach is resided in the fact that the features are so selected that both local and global texture distributions are considered, which is important in liver organ segmentation where neighbouring tissues and organs have similar greyscale distributions. Experiment results of liver segmentation on CT images using the proposed method are presented with performance validation and discussion.

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S. H. Luo, X. C. Li and J. M. Li, "Improvement of Liver Segmentation by Combining High Order Statistical Texture Features with Anatomical Structural Features," Engineering, Vol. 5 No. 5B, 2013, pp. 67-72. doi: 10.4236/eng.2013.55B014.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] A. M. Mharib, A. R. Ramli, S. Mashohor and R. B. Mahmood, “Survey on liver CT image segmentation methods”, Artificial Intelligence Review, Vol. 37, No. 2, 2012, pp. 83-95. doi:10.1007/s10462-011-9220-3
[2] H. Bourquain, et al., “Hepavision2—A Software Assistant for Preoperative Planning in Living Related Liver Transplantation and Oncologic Liver Surgery,” Computer Assisted Radiology&Surgery, 2002, pp. 341-346.
[3] H. P. Meinzer, M. Thorn and C. Cardenas, “Computerized Planning of Liver Surgery: An Overview,” Computers and Graphics, Vol. 26, No. 4, 2002, pp. 569-576.
[4] P. Campadelli, E. Casiraghi and A. Esposito, “Liver Segmentation from Computed Tomography Scans: A Survey and a New Algorithm,” Artificial Intelligence in Medicine, Vol. 45, No. 2-3, 2009, pp. 185-196. doi:10.1016/j.artmed.2008.07.020
[5] S. Luo, Q. Hu, X. He, J. Li, S. J. Jin, S. Chalup and M. Park, “Automatic Liver Parenchyma Segmentation from Abdominal CT Images Using Support Vector Machines,” 2009 IEEE/CME Int. Conf on Complex Medical Engineering, April 9-11, 2009, Tempe, USA, paper 10071.
[6] V. Pamulapati, A. Venkatesan, B. J. Wood and M. G. Linguraru, “Liver Segmental Anatomy and Analysis from Vessel and Tumor Segmentation via Optimized Graph Cuts,” MICCAI'11 Proceedings of the Third international conference on Abdominal Imaging: Computational and Clinical Applications, 2011, pp. 189-197.
[7] T. Dima and J. Domingo, “A Local Level Set Method for Liver Segmentation in Functional MR Imaging,” IEEE Nuclear Science Symposium Conference Record, 2011, pp. 3158-3161.
[8] J. Lu, L. Shi, M. Deng, S. C. H. Yu and P. A. Heng, “An Interactive Approach to Liver Segmentation in CT Based on Deformable Model Integrated with Attractor Force,” Machine Learning and Cybernetics (ICMLC) 2011, pp. 1660-1665.
[9] Y. Zhao, Y. Zan, X. Wang and G. Li, “Fuzzy C-means Clustering-Based Multilayer Perceptron Neural Network for Liver CT Images Automatic Segmentation,” Control and Decision Conference (CCDC) 2010, pp. 3423-3427.
[10] A. Materka and M. Strzelecki, “Texture Analysis Methods - a Review,” Technical Report, Technical University of Lodz, Institute of Electronics, 1998.
[11] R. M. Haralick, “Statistical and Structural Approaches to Texture,” Proceedings of the IEEE, Vol. 67, No. 5, 1979, 786-804. doi:10.1109/PROC.1979.11328
[12] S. Mallat, “Multifrequency Channel Decomposition of Images and Wavelet Models”, IEEE Trans. Acoustic, Speech and Signal Processing, Vol. 37, No. 12, 1989, pp. 2091-2110.
[13] A. Rosenfeld and J. Weszka, “Picture Recognition in Digital Pattern Recognition,”K. Fu (Ed.), Springer-Verlag, 1980, pp. 135-166.doi:10.1007/978-3-642-67740-3_5
[14] J. Daugman, “Uncertainty Relation for Resolution in Space, Spatial Frequency and Orientation Optimised by Two-Dimensional Visual Cortical Filters,” Journal of the Optical Society of America, Vol. 2, 1985, pp. 1160-1169. doi;10.1364/JOSAA.2.001160
[15] N. Cristianini and J. Shawe-Taylor, “An Introduction to Support Vector Machines and Other Kernel-based Learning Methods,” Cambridge University Press, ISBN 0521780195, 2000. doi:10.1017/CBO9780511801389
[16] J. Serra, “Image Analysis and Mathematical Morphology,” Theoretical Advances, New York: Academic, Vol. 2, 1998.
[17] T. Heimann, M. Styner and B. van Ginneken, “3D Segmentation in the Clinic: A Grand Challenge,” MICCAI 2007, the 10th Intel Conf. on Medical Image Computing and Computer Assisted Intervention, 29 Oct. to 2 Nov. 2007, Brisbane, Australia, pp. 7-15.

  
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