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

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.

[1]  H. Peitgen,et al.  HepaVision2 — a software assistant for preoperative planning in living-related liver transplantation and oncologic liver surgery , 2002 .

[2]  Michal Strzelecki,et al.  Texture Analysis Methods - A Review , 1998 .

[3]  Bradford J. Wood,et al.  Liver Segmental Anatomy and Analysis from Vessel and Tumor Segmentation via Optimized Graph Cuts , 2011, Abdominal Imaging.

[4]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[5]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[6]  Stéphane Mallat,et al.  Multifrequency channel decompositions of images and wavelet models , 1989, IEEE Trans. Acoust. Speech Signal Process..

[7]  Elena Casiraghi,et al.  Liver segmentation from computed tomography scans: A survey and a new algorithm , 2009, Artif. Intell. Medicine.

[8]  E. Dura,et al.  A local level set method for liver segmentation in functional MR imaging , 2011, 2011 IEEE Nuclear Science Symposium Conference Record.

[9]  Xiangjian He,et al.  Automatic liver parenchyma segmentation from abdominal CT images using support vector machines , 2009, 2009 ICME International Conference on Complex Medical Engineering.

[10]  Hans-Peter Meinzer,et al.  Computerized planning of liver surgery - an overview , 2002, Comput. Graph..

[11]  Xiaofang Wang,et al.  Fuzzy C-means clustering-based multilayer perceptron neural network for liver CT images automatic segmentation , 2010, 2010 Chinese Control and Decision Conference.

[12]  Lin Shi,et al.  An interactive approach to liver segmentation in CT based on deformable model integrated with attractor force , 2011, 2011 International Conference on Machine Learning and Cybernetics.

[13]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[14]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[15]  B. Ginneken,et al.  3D Segmentation in the Clinic: A Grand Challenge , 2007 .

[16]  Abdul Rahman Ramli,et al.  Survey on liver CT image segmentation methods , 2011, Artificial Intelligence Review.