Application of artificial neural networks for the classification of liver lesions by image texture parameters

Ultrasound in Medicine and Biology

Volumn 22, Issue 9, 1996, Pages 1177-1181

  Sujana, H ^a   Swarnamani, S ^a   Suresh, S ^b  

^a INDIAN INSTITUTE OF TECHNOLOGY MADRAS   (India)

^b MediScan Systems Chennai   (India)

Author keywords

Hemangioma; Malignancy; Neural network classification; Texture parameters; Ultrasonic scan

Indexed keywords

BACKPROPAGATION; IMAGE ANALYSIS; IMAGE QUALITY; MEDICAL IMAGING; NEURAL NETWORKS; TISSUE;

IMAGE TEXTURE PARAMETERS; LIVER LESIONS;

ULTRASONIC IMAGING;

ARTICLE; ARTIFICIAL NEURAL NETWORK; B SCAN; DIAGNOSTIC ACCURACY; DIAGNOSTIC IMAGING; ECHOGRAPHY; LIVER CARCINOMA; LIVER HEMANGIOMA; PRIORITY JOURNAL; RADIOFREQUENCY; SOFT TISSUE;

DIAGNOSIS, DIFFERENTIAL; DISCRIMINANT ANALYSIS; HEMANGIOMA; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LIVER NEOPLASMS; NEURAL NETWORKS (COMPUTER); REPRODUCIBILITY OF RESULTS;

EID: 0030443396     PISSN: 03015629     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0301-5629(96)00144-5     Document Type: Article

References (19)

1. Bolondi L, Labo G, Gandolfi L. Diagnostic ultrasound in gastroenterology. Rome: Piccin/Butterworths; 1984.
2. Chu A, Sehgal CM, Greenleaf JF. Use of gray level value distribution of run lengths for texture analysis. Patt Recog Lett 1990;12:415-420.
3. Garra BS, Krasner BH, Horii SC, Ascher S, Mun SK, Zeman RK. Improving the distinction between benign and malignant breast lesions: the value of sonographic texture analysis. Ultrason Imag 1993;15:267-285.
4. Gebbinck MS, Verhoven JTM, Thijssen JM, Schouten TE. Application of neural networks for the classification of diffuse liver disease by quantitative echography. Ultrason Imag 1993;15:168-169.
5. Goldberg V, Manduca A, Ewert DL, Gisvold JJ, Greenleaf JF. Improvement in specificity of ultrasonography for diagnosis of breast tumors by means of artificial intelligence. Med Phys 1992;19:1475-1481.
6. Haberkorn U, Zuna I, Lorenz A, Zerban H, Layer G. Echographic tissue characterization in diffuse parenchymal liver disease: correlation of image structure with histology. Ultrasonic Imaging 1990;12:155-170.
7. Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybernet 1973;SMC-3:610-621.
8. Haykin S. Neural networks. New York: Macmillan; 1994.
9. Hiroyuki O, Yasuaki M. Sonographic diagnosis of fatty liver using a histogram technique that compares liver and renal cortical echo amplitude. J Clin Ultrasound 1996;24:25-29.
10. Jeffrey RB, Ralls PW. Sonography of the abdomen. New York: Raven Press, 1995.
11. Layer G, Zuna I, Lorenz A, Zerban H, Haberkorn U. Computerized ultrasound B-scan texture analysis of experimental fatty liver disease: influence of total lipid content and fat deposit distribution. Ultrason Imag 1990;12:171-188.
12. Lippmann R. An introduction to computing with neural nets. IEEE ASSP Mag 1987;4:4-22.
13. Ostrem JS, Valdes AD, Edmonds PD. Application of neural nets to ultrasound tissue characterization. Ultrason Imag 1991;13:298-299.
14. Prentza A, Wesseling KH. Catheter-manometer system damped blood pressures detected by neural nets. Med Biol Eng Comput 1995;33:589-595.
15. Rinast E, Linder R, Weiss HD. Neural network approach for computer-assisted interpretation of ultrasound images of the gallbladder. Eur J Radiol 1993;17: 175-178.
16. Robbins SL, Cotran RS. Pathologic basis of disease. Philadelphia: WB Saunders; 1982.
17. Weszka JS, Dyer CR, Rosenfeld A. A comparative study of texture measures for terrain classification. IEEE Trans Syst Man Cybernet 1976;SMC-6:269-285.
18. Wiener SN, Parulekar SG. Scintigraphy and ultrasonography of hepatic hemangioma. Radiology 1979;132:149-153.
19. Zatari D, Botros N, Dunn F. In vivo liver differentiation by ultrasound using an artificial neural network. J Acoust Soc Am 1993;96:376-381.