Texture Analysis for Liver Segmentation and Classification: A Survey

Texture is a combination of repeated patterns with regular/irregular frequency. It can only be visualized but hard to describe in words. Liver structure exhibit similar behavior, it has maximum disparity in intensity texture inside and along boundary which serves as a major problem in its segmentation and classification. Problem gets more complicated when one applies simple segmentation techniques without considering variation in intensity texture. The problem of representing liver texture is solved by encoding it in terms of certain parameters for texture analysis. Numerous textural analysis techniques have been devised for liver classification over the years some of which work equally work well for most of the imaging modalities. Here, we attempt to summarize the efficacy of textural analysis techniques devised for Computed Tomography (CT), Ultrasound and some other imaging modalities like Magnetic Resonance Imaging (MRI), in terms of well-known performance metrics.

[1]  Chia-Hung Lin,et al.  Fractal features classification for liver biopsy images using neural network-based classifier , 2010, 2010 International Symposium on Computer, Communication, Control and Automation (3CA).

[2]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

[3]  Haim Azhari,et al.  Feasibility study of ultrasonic fatty liver biopsy: texture vs. attenuation and backscatter. , 2004, Ultrasound in medicine & biology.

[4]  G. Ravindran,et al.  Optimal Feature Selection and Automatic Classification of Abnormal Masses in Ultrasound Liver Images , 2007, 2007 International Conference on Signal Processing, Communications and Networking.

[5]  C. Sparrow The Fractal Geometry of Nature , 1984 .

[6]  A. Nikita,et al.  Evaluation of Texture Features in Hepatic Tissue Characterization from Non-enhanced CT Images , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

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

[9]  Guitao Cao,et al.  Liver Fibrosis Identification Based on Ultrasound Images , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[10]  K. Dewbury,et al.  The accuracy of ultrasound in the detection of fatty infiltration of the liver. , 1980, The British journal of radiology.

[11]  A. H. Mir,et al.  Texture analysis of CT images , 1995 .

[12]  Weiqi Wang,et al.  Decision of Cirrhosis Using Liver's Ultrasonic Images , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[13]  A. Ahmadian,et al.  A texture classification method for diffused liver diseases using Gabor wavelets , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[14]  A.N.J. Raj,et al.  Estimation of Image Magnification Using Phase Correlation , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[15]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[16]  Marek Kretowski,et al.  Texture-Based Classification of Hepatic Primary Tumors in Multiphase CT , 2004, MICCAI.

[17]  Sotiris Pavlopoulos,et al.  Computer assisted characterization of liver tissue using image texture analysis techniques on B-scan images , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[18]  Chandan Kundu,et al.  Enhancement of textural features in normal & diseased ultra sonogram of liver by Gaussian smoothing , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

[19]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[20]  Zhen Zhao,et al.  Texture analysis of ultrasonic liver images based on spatial domain methods , 2010, 2010 3rd International Congress on Image and Signal Processing.

[21]  Kee Tung. Wong,et al.  Texture features for image classification and retrieval. , 2002 .

[22]  Jean-Louis Coatrieux,et al.  A preliminary study of moment-based texture analysis for medical images , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[23]  V. Sadasivam,et al.  Wavelet based texture analysis of Liver tumor from Computed Tomography images for characterization using Linear Vector Quantization Neural Network , 2006, 2006 International Conference on Advanced Computing and Communications.

[24]  U Ranft,et al.  Random field models in the textural analysis of ultrasonic images of the liver , 1996, IEEE Trans. Medical Imaging.

[25]  M. Teague Image analysis via the general theory of moments , 1980 .

[26]  Xiangrong Zhou,et al.  Improving the Classification of Cirrhotic Liver by using Texture Features , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[27]  Aleksandra Mojsilovic,et al.  Characterization of visually similar diffuse diseases from B-scan liver images with the nonseparable wavelet transform , 1997, Proceedings of International Conference on Image Processing.

[28]  D. Koutsouris,et al.  Computer assisted characterization of diffused liver disease using image texture analysis techniques on B-scan images , 1997, 1997 IEEE Nuclear Science Symposium Conference Record.

[29]  K. Ghosh,et al.  Corroborating the Subjective Classification of Ultrasound Images of Normal and Fatty Human Livers by the Radiologist through Texture Analysis and SOM , 2007, 15th International Conference on Advanced Computing and Communications (ADCOM 2007).

[30]  S. Nawaz,et al.  Hepatic lesions classification by ensemble of SVMs using statistical features based on co-occurrence matrix , 2008, 2008 4th International Conference on Emerging Technologies.

[31]  P. Srinivasan,et al.  Automatic Classification of Focal Lesions in Ultrasound Liver Images using Principal Component Analysis and Neural Networks , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[33]  Ulrich Ranft Texture Analysis using Random Field Models exemplified on Ultrasonic Images of the Liver , 1987 .

[34]  Sotiris Pavlopoulos,et al.  Evaluation of texture analysis techniques for quantitative characterization of ultrasonic liver images , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[35]  V. Sadasivam,et al.  Automatic Segmentation and Classification of Diffused Liver Diseases using Wavelet Based Texture Analysis and Neural Network , 2005, 2005 Annual IEEE India Conference - Indicon.

[36]  B B Gosink,et al.  Accuracy of ultrasonography in diagnosis of hepatocellular disease. , 1979, AJR. American journal of roentgenology.

[37]  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.

[38]  Yung-Chang Chen,et al.  Texture features for classification of ultrasonic liver images , 1992, IEEE Trans. Medical Imaging.

[39]  L. Ganesan,et al.  Orthogonal Moments Based Texture Analysis of CT Liver Images , 2007 .

[40]  W. J. Lorenz,et al.  Diagnostic accuracy of computerized B‐scan texture analysis and conventional ultrasonography in diffuse parenchymal and malignant liver disease , 1985, Journal of clinical ultrasound : JCU.

[41]  M.H. Mohamed,et al.  An efficient clustering based texture feature extraction for medical image , 2008, 2008 11th International Conference on Computer and Information Technology.

[42]  S. Gunn Support Vector Machines for Classification and Regression , 1998 .

[43]  Konstantina S. Nikita,et al.  A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier , 2003, IEEE Transactions on Information Technology in Biomedicine.

[44]  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.

[45]  K. Blekas,et al.  Fuzzy neural network-based texture analysis of ultrasonic images , 2000, IEEE Engineering in Medicine and Biology Magazine.

[46]  Konstantina S. Nikita,et al.  Characterization of CT liver lesions based on texture features and a multiple neural network classification scheme , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[47]  Yali Huang,et al.  Texture Analysis of Ultrasonic Liver Image Based on Wavelet Transform and Probabilistic Neural Network , 2008, 2008 International Conference on BioMedical Engineering and Informatics.