Neural network ensemble based CAD system for focal liver lesions from B-mode ultrasound

Journal of Digital Imaging

Volumn 27, Issue 4, 2014, Pages 520-537

  Virmani, Jitendra ^a   Kumar, Vinod ^b   Kalra, Naveen ^c   Khandelwal, Niranjan ^c  

^a JAYPEE UNIVERSITY OF INFORMATION TECHNOLOGY   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

^c POSTGRADUATE INSTITUTE OF MEDICAL EDUCATION AND RESEARCH   (India)

Author keywords

B mode ultrasound; Computer aided diagnostic system; Focal liver lesions; Neural network ensemble; Principal component analysis; Texture analysis

Indexed keywords

COMPUTER AIDED ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; TEXTURES; ULTRASONICS;

CLASSIFICATION ACCURACY; COMPUTER AIDED DIAGNOSTICS; DIFFERENTIAL DIAGNOSIS; FOCAL LIVER LESIONS; HEPATOCELLULAR CARCINOMA; NEURAL NETWORK ENSEMBLES; TEXTURAL CHARACTERISTIC; TEXTURE ANALYSIS;

COMPUTER AIDED DIAGNOSIS;

ARTIFICIAL NEURAL NETWORK; COMPUTER ASSISTED DIAGNOSIS; DIFFERENTIAL DIAGNOSIS; ECHOGRAPHY; HUMAN; LIVER; LIVER TUMOR; PRINCIPAL COMPONENT ANALYSIS; PROCEDURES; REPRODUCIBILITY;

DIAGNOSIS, DIFFERENTIAL; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LIVER; LIVER NEOPLASMS; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY OF RESULTS;

EID: 84904391969     PISSN: 08971889     EISSN: 1618727X     Source Type: Journal    
DOI: 10.1007/s10278-014-9685-0     Document Type: Article

References (55)

1. Namasivayam S, Salman K, Mittal PK, Martin D, Small WC: Hypervascular hepatic focal lesions: spectrum of imaging features. Curr Probl Diagn Radiol 36(3):107-123, 2007 (Pubitemid 46672791)
2. Tiferes DA, D'lppolito G: Liver neoplasms: imaging characterization. Radiol Bras 41(2):119-127, 2008
3. Wernecke K, Vassallo P: The distinction between benign and malignant liver tumors on sonography: value of a hypoechoic halo. Am J Radiol 159:1005-1009, 1992
4. Mittelstaedt CA: Ultrasound as a useful imaging modality for tumor detection and staging. Cancer Res 1980(40):3072-3078, 1980
5. Bates J: Abdominal Ultrasound How Why and When, 2nd edition. Churchill Livingstone, Oxford, 2004, pp 80-107
6. Soye JA, Mullan CP, Porter S, Beattie H, Barltrop AH, Nelson WM: The use of contrast-enhanced ultrasound in the characterization of focal liver lesions. Ulster Med J 76(1):22-25, 2007 (Pubitemid 46157345)
7. Pen JH, Pelckmans PA, Van Maercke YM, Degryse HR, De Schepper AM: Clinical significance of focal echogenic liver lesions. Gastrointest Radiol 11(1):61-66, 1986
8. Colombo M, Ronchi G: Focal Liver Lesions - Detection, Characterization, Ablation. Springer, Berlin, 2005, pp 167-177
9. Harding J, Callaway M: Ultrasound of focal liver lesions. Rad Mag 36(424):33-34, 2010
10. Jeffery RB, Ralls PW: Sonography of Abdomen. Raven, New York, 1995
11. Tsurusaki M, Kawasaki R, Yamaguchi M, Sugimoto K, Fukumoto T, Ku Y, Sugimura K: Atypical hemangioma mimicking hepatocellular carcinoma with a special note on radiological and pathological findings. Jpn J Radiol 27(3):156-160, 2009
12. Sandulescu L, Saftoiu A, Dumitrescu D, Ciurea T: Real-time contrast-enhanced and real-time virtual sonography in the assessment of benign liver lesions. J Gastrointest Liver Dis 17(4):475-478, 2008
13. Nielsen MB, Bang N: Contrast enhanced ultrasound in liver imaging. Eur J Radiol 51:S3-S8, 2004 (Pubitemid 38845007)
14. Marsh JI, Gibney RG, Li DKB: Hepatic hemangioma the presence of fatty infiltration: an atypical sonographic appearance. Gastrointest Radiol 14:262-264, 1989 (Pubitemid 19129361)
15. Mittal D, Kumar V, Saxena SC, Khandelwal N, Kalra N: Neural network based focal liver lesion diagnosis using ultrasound images. Int J Comput Med Imaging Graph 35(4):315-323, 2011
16. Virmani J, Kumar V, Kalra N, Khandelwal N: Characterization of primary and secondary malignant liver lesions from B-mode ultrasound. J Digit Imaging 26(6):1058-1070, 2013
17. Vilgrain V, Boulos L, Vullierme MP, Denys A, Terris B, Menu Y: Imaging of atypical hemangiomas of the liver with pathologic correlation. Radiographics 20(2):379-397, 2000
18. Virmani J, Kumar V, Kalra N, Khandelwal N: SVM-based characterization of liver ultrasound images using wavelet packet texture descriptors. J Digit Imaging 26(3):530-543, 2013
19. Minhas F, Sabih D, Hussain M: Automated classification of liver disorders using ultrasound images. J Med Syst 36(5):3163-3172, 2011
20. Virmani J, Kumar V, Kalra N, Khandelwal N: Prediction of liver cirrhosis based on multiresolution texture descriptors from B-mode ultrasound. Int J Converg Comput 1(1):19-37, 2013
21. Virmani J, Kumar V, Kalra N, Khandelwal N: PCA-SVM based CAD system for focal liver lesions from B-mode ultrasound. Def Sci J 63(5):478-486, 2013
22. Virmani J, Kumar V, Kalra N, Khandelwal N: A comparative study of computer-aided classification systems for focal hepatic lesions from B-mode ultrasound. J Med Eng Technol 37(4):292-306, 2013
23. Yoshida H, Casalino DD, Keserci B, Coskun A, Ozturk O, Savranlar A: Wavelet packet based texture analysis for differentiation between benign and malignant liver tumors in ultrasound images. Phys Med Biol 48:3735-3753, 2003 (Pubitemid 37483478)
24. Scheible W, Gossink BB, Leopold G: Gray scale echo graphic patterns of hepatic metastatic disease. Am J Roentgenol 129:983-987, 1977 (Pubitemid 8235327)
25. Albrecht T, Hohmann J, Oldenburg A, Wolf K: Detection and characterisation of liver metastases. Eur Radiol Suppl 14(S8):25-P33, 2004 (Pubitemid 40425144)
26. Di Martino M, De Filippis G, De Santis A, Geiger D, Del Monte M, Lombardo CV, Rossi M, Corradini SG, Mennini G, Catalano C: Hepatocellular carcinoma in cirrhotic patients: prospective comparison of US, CT and MR imaging. Eur Radiol 23(4):887-896, 2013
27. Kimura Y, Fukada R, Katagiri S, Matsuda Y: Evaluation of hyperechoic liver tumors in MHTS. J Med Syst 17(3/4):127-132, 1993 (Pubitemid 23277382)
28. Sujana S, Swarnamani S, Suresh S: Application of artificial neural networks for the classification of liver lesions by image texture parameters. Ultrasound Med Biol 22(9):1177-1181, 1996 (Pubitemid 27024658)
29. Poonguzhali S, Deepalakshmi, Ravindran G: Optimal feature selection and automatic classification of abnormal masses in ultrasound liver images. In: Proceedings of IEEE International Conference on Signal Processing, Communications and Networking, ICSCN'07, 503-506, 2007 (Pubitemid 47304163)
30. Kim SH, Lee JM, Kim KG, Kim JH, Lee JY, Han JK, Choi BI: Computer-aided image analysis of focal hepatic lesions in ultrasonography: preliminary results. Abdom Imaging 34(2):183-191, 2009
31. Huang YL, Wang KL, Chen DR: Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Comput Appl 15:164-169, 2006 (Pubitemid 43190390)
32. Nandi RJ, Nandi AK, Rangayyan RM, Scutt D: Classification of breast masses in mammograms using genetic programming and feature selection. Med Biol Eng Comput 44(8):683-694, 2006 (Pubitemid 44203439)
33. Diao XF, Zhang XY, Wang TF, Chen SP, Yang Y, Zhong L: Highly sensitive computer aided diagnosis system for breast tumor based on color Doppler flow images. J Med Syst 35(5):801-809, 2011
34. Moayedi F, Azimifar Z, Boostani R, Katebi S: Contourlet based mammography mass classification. In: Proceedings of ICIAR 2007. LNCS 4633:923-934, 2007
35. Alto H, Rangayyan R: An indexed atlas of digital mammograms for computer-aided diagnosis of breast cancer. Ann Telecommun 58:820-835, 2003
36. Huang YL, Chen DR, Jiang YR, Kuo J, Wu HK, Moon WK: Computer-aided diagnosis using morphological features for classifying breast lesions on ultrasound. Ultrasound Obstet Gynecol 32:565-572, 2008
37. André T, Rangayyan R: Classification of breast masses in mammograms using neural networks with shape, edge sharpness, and texture features. J Electron Imaging 15(01):684481, 2006 (Pubitemid 43810441)
38. Rangayyan RM, Nguyen TM: Pattern classification of breast masses via fractal analysis of their contours. Int Congr Ser 1281:1041-1046, 2005 (Pubitemid 43693639)
39. Lee WL, Hsieh KS, Chen YC: A study of ultrasonic liver images classification with artificial neural networks based on fractal geometry and multiresolution analysis. Biomed Eng Appl Basis Commun 16(2):59-67, 2004
40. Badawi AM, Derbala AS, Youssef ABM: Fuzzy logic algorithm for quantitative tissue characterization of diffuse liver diseases from ultrasound images. Int J Med Inform 55:135-147, 1999 (Pubitemid 29413157)
41. Fukunaga K: Introduction to Statistical Pattern Recognition. Academic, New York, 1990
42. Kadah YM, Farag AA, Zurada JM, Badawi AM, Youssef AM: Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images. IEEE Trans Med Imaging 15(4):466-478, 1996 (Pubitemid 126780336)
43. Virmani J, Kumar V, Kalra N, Khandelwal N: A rapid approach for prediction of liver cirrhosis based on first order statistics. In: Proceedings of IEEE International Conference on Multimedia, Signal Processing and Communication Technologies, IMPACT-2011, 212-215, 2011
44. Haralick R, Shanmugam K, Dinstein I: Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3(6):610-121, 1973
45. Virmani J, Kumar V, Kalra N, Khandelwal N: Prediction of cirrhosis based on singular value decomposition of gray level cooccurrence matrix and a neural network classifier. In: Proceedings of IEEE International Conference on Developments in E-systems Engineering, DeSe-2011, 146-151, 2011
46. Virmani J, Kumar V, Kalra N, Khandelwal N: SVM-based characterisation of liver cirrhosis by singular value decomposition of GLCM matrix. Int J Artif Intell Soft Comput 4(1):276-296, 2013
47. Galloway RMM: Texture analysis using gray level run lengths. Comput Graph Image Process 4:172-179, 1975
48. Chu A, Sehgal CM, Greenleaf JF: Use of gray value distribution of run lengths for texture analysis. Pattern Recogn Lett 11:415-420, 1990
49. Dasarathy BV, Holder EB: Image characterizations based on joint gray level-run length distributions. Pattern Recogn Lett 12:497-502, 1991
50. Lee C, Chen S H: Gabor wavelets and SVM classifier for liver diseases classification from CT images. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, 548-552, 2006
51. Laws KI: Rapid texture identification. SPIE Proc Semin Image Process Missile Guid 238:376-380, 1980
52. Sharma M, Markou M, Singh S: Evaluation of texture methods for image analysis. In: Proceedings of the Seventh Australian and New Zealand Intelligent Information Systems Conference, 117-121, 2001
53. Virmani J, Kumar V, Kalra N, Khandelwal N: Prediction of cirrhosis from liver ultrasound B-mode images based on Laws' masks analysis. In: Proceedings of IEEE International Conference on Image Information Processing, ICIIP-2011, 1-5, 2011
54. Kadir A, Nugroho LE, Susanto A, Santosa PI: Performance improvement of leaf identification system using principal component analysis. Int J Adv Sci Technol 44:113-124, 2012
55. Du C, Linker R, Shaviv A: Identification of agricultural Mediterranean soils using mid-infrared photoacoustic spectroscopy. Geoderma 143(1-2):85-90, 2008