An automated neural-fuzzy approach to malignant tumor localization in 2D ultrasonic images of the prostate

Journal of Digital Imaging

Volumn 24, Issue 3, 2011, Pages 411-423

  Mohamed, Samar Samir ^a   Li, J M ^a   Salama, M M A ^a   Freeman, G H ^a   Tizhoosh, H R ^a   Fenster, A ^b   Rizkalla, K ^b  

^a UNIVERSITY OF WATERLOO   (Canada)

^b UNIVERSITY OF WESTERN ONTARIO   (Canada)

Author keywords

Fuzzy inference; Malignant tumor localization; Prostate cancer; RNN; Spatial feature; Textural feature; Tissue segmentation; TRUS

Indexed keywords

MALIGNANT TUMOR LOCALIZATION; PROSTATE CANCERS; RNN; SPATIAL FEATURES; TEXTURAL FEATURE; TISSUE SEGMENTATION; TRUS;

AUTOMATION; DECISION SUPPORT SYSTEMS; DISEASES; FUZZY INFERENCE; HISTOLOGY; MEMBERSHIP FUNCTIONS; RECURRENT NEURAL NETWORKS; TUMORS; ULTRASONIC IMAGING;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; ECHOGRAPHY; FUZZY LOGIC; HUMAN; IMAGE PROCESSING; MALE; METHODOLOGY; PROSTATE; PROSTATE TUMOR; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; FUZZY LOGIC; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MALE; PATTERN RECOGNITION, AUTOMATED; PROSTATE; PROSTATIC NEOPLASMS; ROC CURVE; SENSITIVITY AND SPECIFICITY;

EID: 80052911915     PISSN: 08971889     EISSN: 1618727X     Source Type: Journal    
DOI: 10.1007/s10278-010-9301-x     Document Type: Article

References (31)

1. National Cancer Institute of Canada. Canadian cancer statistics 2008. Toronto, Canada, 2009.
2. Heidenreich A, Bolla M, Joniau S, van der Kwast TH, Matveev V, Mason, Mottet N, Schmid H-P, Wiegel T, Zattoni F. Guidelines on Prostate Cancer. Eur Urol 53(1):68-80, 2008.
3. Presti Jr, JC, Chang JJ, Bhargava V, Shinohara K: The optimal systematic prostate biopsy scheme should include 8 rather than 6 biopsies: results of a prospective clinical trial. J Urol 163(1):163-167, 2000.
4. Presti Jr, JC, O'Dowd GJ, Miller MC, Mattu R, Veltri RW: Extended peripheral zone biopsy schemes increase cancer detection rates and minimize variance in prostate specific antigen and age related cancer rates: results of a community multi-practice study. J Urol 169(1):125-129, 2003.
5. Ethan J. Halpern, Stephen E. Strup. Using gray-scale and color and power doppler sonography to detect prostatic cancer. Am J Roentgenol 174:623-627, 2000.
6. Turgut AT, Olcucuoglu E, Kosar P, Geyik PO, Kosar U, Dogra V: Power doppler ultrasonography of the feeding arteries of the prostate gland: a novel approach to the diagnosis of prostate cancer. J Ultrasound Med 26(7):875-883, 2007.
7. Halpern EJ, Frauscher F, Rosenberg M, Gomella LG: Directed biopsy during contrast-enhanced sonography of the prostate. Am J Roentgenol 178(4):915-919, 2002.
8. Schwartz L: Imaging and prostate cancer. J Radiation Oncology 36(1-supplement):118, 1996.
9. Clements R: Has ultrasonography a role for screening for prostatic cancer? Eur Radiol 7:217-223, 1997.
10. Bushberg JT, Seibert JA, Leidholdt Jr, EM, Boone JM: The essential physics of medical imaging, 2nd edition. Lippincott, Williams & Wilkins, Philadelphia, 2002, pp 469- 553.
11. Chiu B, Freeman GH, Salama MMA, Fenster A: Prostate segmentation algorithm using dyadic wavelet transform and discrete dynamic contour. Phys Med Biol 49(21):4943-4960, 2004.
12. Scheipers U, Ermert H, Sommerfeld H-J, Garcia-Schurmann M, Senge T, Philippou S: Ultrasonic multifeature tissue characterization for prostate diagnostics. Ultrasound Med Biol 29(8):1137-1149, 2003.
13. Mohamed SS, Salama MMA, Kamel M, El-Sadaany EF, Rizkalla K, Chin J: Prostate cancer multi-feature analysis using TRUS images. Phys Med Biol 50(15):N175-N185, 2005.
14. Mohamed SS, Li J, Salama MMA, Freeman G: Prostate tissue texture feature extraction for suspicious regions identification on TRUS images. J Digit Imaging 22(5):503-518 (doi 10.1007/s10278-008-9124-1), 2009.
15. Mohamed SS, Salama MMA: Spectral clustering for TRUS images. in BioMedical Engineering OnLine, 2007, 6:10 (15 March 2007).
16. Chen ME, Johnston DA, Tang K, Babaian RJ, Troncoso P: Detailed mapping of prostate carcinoma foci. Cancer 89 (8):1800-1809, 2000.
17. Loch T, Leuschner I, Genberg C, Weichert-Jacobsen K, Kuppers F, Retz M, Lehmann J, Yfantis E, Evans M, Tsarev V, Stockle M: Future trends in transrectal ultrasound. Artificial Neural Network Analysis (ANNA) in the detection and staging of prostate cancer. Der Urologe A 39(4):341-347, 2000.
18. McNitt-Gray MF, Huang HK, Sayre JW: Feature selection in the pattern classification problem of digital chest radiograph segmentation. IEEE Trans Med Imaging 14:537- 547, 1995.
19. Hassanien Aboul Ella: Intelligence techniques for prostate ultrasound image analysis. International Journal of Hybrid Intelligent Systems 6(3):155-167, 2009.
20. Mohamed SS, Salama MMA: Prostate cancer spectral multi-feature analysis using TRUS images. IEEE Trans Med Imag 27(4):548-556, 2008.
21. Gelenbe E, Feng YT, Ranga K, Krishnan R: Neural network methods for volumetric magnetic resonance imaging of the human brain. Proc IEEE 84:1488-1496, 1996.
22. Gelenbe E: Random neural networks with negative and positive signals and product form solution. Neural Comput 1 (4):502-510, 1989.
23. Gelenbe E: Stability of the random neural network model. Neural Comput 2(2):239-247, 1990.
24. Gelenbe E: Learning in the recurrent random neural network. Neural Comput 5:154-164, 1993.
25. Gelenbe E, Mao ZH, Li YD: Function approximation by random neural networks with a bounded number of layers. J Differ Equ Dyn Syst 12(1-2):143-170, 2004.
26. Gelenbe E, Hussain K: Learning in the multiple class random neural network. IEEE Trans Neural Netw 13(6):1257- 1267, 2002.
27. Zadeh LA: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern 3:28-44, 1973.
28. Zadeh LA: Knowledge representation in fuzzy logic. IEEE Trans Knowl Data Eng 1:89-100, 1989.
29. Obuchowski NA, McClish DK: Sample size determination for diagnostic accuracy studies involving binomial ROC curve indices. Stastics in Medicine 16(13):1529-1542, 1997.
30. Hanley JA, McNeil BJ: The meaning and use of the area under the receiver operation characteristic (ROC) curve. Radiology 143:29-36, 1982.
31. Choi BC: Slopes of a receiver operating characteristic curve and likelihood ratios for a diagnostic test. Am J Epidemiol 148(11):1127-1132, 1998.