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A Study of Textural Analysis Methods for the Diagnosis of Liver Diseases from Abdominal Computed Tomography

by Gunasundari S, Janakiraman S
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 74 - Number 11
Year of Publication: 2013
Authors: Gunasundari S, Janakiraman S
10.5120/12927-9800

Gunasundari S, Janakiraman S . A Study of Textural Analysis Methods for the Diagnosis of Liver Diseases from Abdominal Computed Tomography. International Journal of Computer Applications. 74, 11 ( July 2013), 7-12. DOI=10.5120/12927-9800

@article{ 10.5120/12927-9800,
author = { Gunasundari S, Janakiraman S },
title = { A Study of Textural Analysis Methods for the Diagnosis of Liver Diseases from Abdominal Computed Tomography },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 74 },
number = { 11 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 7-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume74/number11/12927-9800/ },
doi = { 10.5120/12927-9800 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:41:57.635653+05:30
%A Gunasundari S
%A Janakiraman S
%T A Study of Textural Analysis Methods for the Diagnosis of Liver Diseases from Abdominal Computed Tomography
%J International Journal of Computer Applications
%@ 0975-8887
%V 74
%N 11
%P 7-12
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Liver diseases are considered seriously because liver is a vital organ to human beings. Computer aided liver analysis is a technique that can help radiologists to accurately identify diseases that can help in reducing the risk of liver surgery. The computer aided diagnosis (CAD) system consists of the segmentation of liver and lesion, extraction of features from a lesion and characterization of liver diseases by means of a classifier. In the last decade, the use of many segmentation techniques and classifier systems have been proposed by many authors with the intention to increase the performance of CAD systems This article focuses on various textural analysis methods used so far for the classification of liver diseases from abdominal Computed Tomography scans. It reviews the techniques and results of the various methods are analyzed and summarized. The future direction for the research is also discussed

References
  1. Srinivasan GN, and Shobha G. (2008) Statistical Texture Analysis. In: Proc. of World Academy of Science, Engineering And Technology 36: 1264-1269
  2. Haralick RM, Shanmugam K, Dinstein I (1973) Texture features for image classification. IEEE Transaction on System, Man and Cybernetics SMC-3 (6) : 610-621
  3. Rathore S, Iftikhar MA, Hussain M, Jalil A (2011) Texture analysis for liver segmentation and classification: a survey. In: Proc. of International Conference of IEEE on Frontiers of Information Technology 121-126
  4. Huang, YL, Chen JH, Shen WC (2006) Diagnosis of Hepatic Tumors with Texture Analysis in Non enhanced Computed Tomography Images. Acad Radiol 2006 13:713–720
  5. Lee CC, Chiang YC, Tsai CL, Chen SH (2007 a) Distinction of Liver Disease from CT images using Kernel-based Classifiers. ICMED, 1:2:113-120
  6. Lee CC, Chen SH, Chiang YC (2007 b) Classification of Liver Disease from CT Images Using a Support Vector Machine. Journal of Advanced Computational Intelligence and Intelligent Informatics 11:4: 396-402
  7. Nawaz S (2008) Hepatic lesions classification by ensemble of SVMs using statistical features based on co-occurrence matrix. In: Proc. of International Conference of IEEE on Emerging Technologies 21-26
  8. Wang L, Zhang Z , Liu J, Jiang B, Duan X, Xie Q, Hu D, Li Z (2009) Classification of Hepatic Tissues from CT Images Based on Texture Features and Multiclass Support Vector Machines. In: Proc of International Symposium on Neural Netwroks: Advances in Neural Network
  9. Mir AH, Hanmandlu M, Tandon SN (1995) Texture analysis of CT images. IEEE Engineering in Medicine and Biology 14: 6:781-786
  10. Chen EL, Chung PC, Chen CL, Tsai HM, Chang CI (1998) An Automatic Diagnostic System for CT Liver Image Classification. IEEE Transactions on Biomedical Engineering 45:6:783-794
  11. Valavanis I, Mougiakakou SG, Nikita KS, Nikita A (2004) Computer Aided Diagnosis of CT Focal Liver Lesions by an Ensemble of Neural Network and Statistical Classifier. In: Proc. of International Conference of IEEE on Neural Networks 3:1929-1934
  12. Mala K, Sadasivam V (2005) Automatic segmentation and classification of diffused liver diseases using wavelet based texture analysis and Neural Network. In Proc of International Conference of IEEE on INDICON 216-219
  13. Mala K, Sadasivam V (2006 a) Wavelet based texture analysis of Liver tumor from Computed Tomography images for characterization using Linear Vector Quantization Neural Network. In: Proc. Of International conference of IEEE on Advanced Computing and Communication 267 - 270
  14. Mala K, Sadasivam V, S. Alagappan (2006 b) Neural Network based Texture Analysis of Liver Tumor from Computed Tomography Images. International Journal of Biological and Life Sciences 2:1:33-40
  15. Mala K , Sadasivam S (2010) Classification of Fatty and Cirrhosis Liver Using Wavelet-Based Statistical Texture Features and Neural Network Classifier. International Journal of Software Informatics 4:2:151-163
  16. Kumar SS, Moni RS (2010 a) Diagnosis of Liver Tumor from CT Images using Curvelet Transform. International Journal on Computer Science and Engineering 2:4: 1173-1178
  17. Kumar SS, Moni RS (2010 b) Diagnosis of Liver Tumor from CT Images using Curvelet Transform. International Journal of Computer Application Special Issue on CASCT 1:1-6
  18. Lakshmana B, Arakeri MP (2011) Texture based characterization of liver tumor on computed tomography images. In: Proc. of the International Conference & Workshop on Emerging Trends in Technology Pages 141-144
  19. Kondo T, Ueno J, Takao S (2011) Hybrid GMDH-type neural network using artificial intelligence and its application to medical image diagnosis of liver cancer. In: Proc. Of International Symposium of IEEE on System Integration 1101-1106
  20. Kumar SS, Moni RS, Rajeesh J (2011) Contourlet Transform Based Computer-Aided Diagnosis System for Liver Tumors on Computed Tomography Images. In: Proc. of International Conference of IEEE on Signal Processing, Communication, Computing and Networking Technologies 217 - 222
  21. Gunasundari S, Suganya Ananthi M (2012) Comparison and Evaluation of Methods for Liver Tumor Classification from CT Datasets. International Journal of Computer Applications 39:18:46 -51
  22. Kumar SS, Moni RS, Rajeesh J (2012) Liver tumor diagnosis by gray level and contourlet coefficients texture analysis. International Conference on Computing, Electronics and Electrical Technologies 557 – 562
  23. Glestos M, Mougiakakou SG, Matsopoulos GK. , Nikita KS, Nikita, AS, Kelekis D (2001) Classification of hepatic lesions from CT images using texture features and neural networks. In: Proc. Of 23rd Annual EMBS International Conference of IEEE on Engineering in Medicine and Biology Society 3: 2748–2752
  24. Gletsos M, Mougiakakou SG, Matsopoulos GK, Nikita KS, Nikita AS, Kelekis D (2003) A Computer-Aided Diagnostic System to Characterize CT Focal Liver Lesions: Design and Optimization of a Neural Network Classifier. IEEE Transactions on Information Technology in Biomedicine 7:3:153 – 162
  25. Mougiakakou SG, Valavanis I, Nikita KS, Nikita A, Kelekis D (2003) Characterization of CT liver lesions based on texture features and a multiple Neural Network classification scheme. In: Proc of lntemational Conference of IEEE on Engineering in Medicine and Biology Society, 2:1287-1290
  26. Mougiakakou SG, Valavanis I, Nikita A, Nikita KS (2007) Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers. Artificial Intelligence in Medicine 41:25—37
  27. Bharathi VS, Vijilious MAL, Ganesan L (2007) Orthogonal Moments based texture analysis of CT liver images. In: Proc. Of International Conference of IEEE on Computational Intelligence and Multimedia Applications 2:249-253
  28. Bharathi VS, Ganesan L (2008) Orthogonal moments based texture analysis of CT liver images. Pattern Recognition Letters 29:1868–1872
Index Terms

Computer Science
Information Sciences

Keywords

CT Textural Features Neural Network Co occurrence Matrix CAD Feature Selection Liver

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