Journal of Biomedical Physics and Engineering

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Staff Members

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

News

Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI

Document Type : Original Research

Authors

Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Abstract

Background: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) provides functional information on the microcirculation in tissues by analyzing the enhancement kinetics which can be used as biomarkers for prostate lesions detection and characterization.Objective: The purpose of this study is to investigate spatiotemporal patterns of tumors by extracting semi-quantitative as well as wavelet-based features, both extracted from pixel-based time-signal intensity curves to segment prostate lesions on prostate DCE-MRI. Methods: Quantitative dynamic contrast-enhanced MRI data were acquired on 22 patients. Optimal features selected by forward selection are used for the segmentation of prostate lesions by applying fuzzy c-means (FCM) clustering. The images were reviewed by an expert radiologist and manual segmentation performed as the ground truth. Results: Empirical results indicate that fuzzy c-mean classifier can achieve better results in terms of sensitivity, speciï‌city when semi-quantitative features were considered versus wavelet kinetic features for lesion segmentation (Sensitivity of 87.58% and 75.62%, respectively) and (Specificity of 89.85% and 68.89 %, respectively).Conclusion: The proposed segmentation algorithm in this work can potentially be implemented for automatic prostate lesion detection in a computer aided diagnosis scheme and combined with morphologic features to increase diagnostic credibility

Keywords

References
  1. Stamey TA, Caldwell M, McNeal JE, Nolley R, Hemenez M, Downs J. The prostate specific antigen era in the United States is over for prostate cancer: what happened in the last 20 years? J Urol. 2004;172:1297-301. doi.org/10.1097/01.ju.0000139993.51181.5d. PubMed PMID: 15371827.
  2. Artan Y, Langer DL, Haider MA, van der Kwast TH, Evans AJ, Wernick MN, et al. Prostate cancer segmentation with multispectral MRI using cost-sensitive conditional random fields. 28 June-1 July 2009 . Boston: Biomedical Imaging: From Nano to Macro, 2009 ISBI’09 IEEE International Symposium on; 2009.
  3. Verma S, Turkbey B, Muradyan N, Rajesh A, Cornud F, Haider MA, et al. Overview of dynamic contrast-enhanced MRI in prostate cancer diagnosis and management. AJR Am J Roentgenol. 2012;198:1277-88. doi.org/10.2214/AJR.12.8510. PubMed PMID: 22623539.
  4. Mostaar A, Ashtiyani M, Lavasany SN, Rexhepi AH, Kongoli R, Dey A, et al. AAn Improved Ant Colony Algorithm Optimization for Automated MRI Segmentation Using Probabilistic Atlas. Int J Innov Res Sci Eng. 2015;3:399, 406.
  5. Turkbey B, Bernardo M, Merino MJ, Wood BJ, Pinto PA, Choyke PL. MRI of localized prostate cancer: coming of age in the PSA era. Diagn Interv Radiol. 2012;18:34-45. PubMed PMID: 21922459.
  6. Puech P, Betrouni N, Makni N, Dewalle AS, Villers A, Lemaitre L. Computer-assisted diagnosis of prostate cancer using DCE-MRI data: design, implementation and preliminary results. Int J Comput Assist Radiol Surg. 2009;4:1-10. doi.org/10.1007/s11548-008-0261-2. PubMed PMID: 20033597.
  7. van Dorsten FA, van der Graaf M, Engelbrecht MR, van Leenders GJ, Verhofstad A, Rijpkema M, et al. Combined quantitative dynamic contrast-enhanced MR imaging and 1H MR spectroscopic imaging of human prostate cancer. Journal of Magnetic Resonance Imaging. 2004;20:279-87. doi.org/10.1002/jmri.20113.
  8. Futterer JJ, Heijmink SW, Scheenen TW, Veltman J, Huisman HJ, Vos P, et al. Prostate cancer localization with dynamic contrast-enhanced MR imaging and proton MR spectroscopic imaging 1. Radiology. 2006;241:449-58. doi.org/10.1148/radiol.2412051866.
  9. Yankeelov TE, Gore JC. Dynamic Contrast Enhanced Magnetic Resonance Imaging in Oncology: Theory, Data Acquisition, Analysis, and Examples. Curr Med Imaging Rev. 2009;3:91-107. doi.org/10.2174/157340507780619179. PubMed PMID: 19829742. PubMed PMCID: 2760951.
  10. Birgani PM, Ashtiyani M, editors. Wireless Real-time Brain Mapping. 27-30 Nov. 2006. Guilin: Communication Technology, 2006 ICCT’06 International Conference on; 206.
  11. Engelbrecht MR, Huisman HJ, Laheij RJ, Jager GJ, van Leenders GJ, Hulsbergen-Van De Kaa CA, et al. Discrimination of prostate cancer from normal peripheral zone and central gland tissue by using dynamic contrast-enhanced MR imaging. Radiology. 2003;229:248-54. doi.org/10.1148/radiol.2291020200. PubMed PMID: 12944607.
  12. Artan Y, Haider MA, Langer DL, van der Kwast TH, Evans AJ, Yang Y, et al. Prostate cancer localization with multispectral MRI using cost-sensitive support vector machines and conditional random fields. IEEE Trans Image Process. 2010;19:2444-55. doi.org/10.1109/TIP.2010.2048612. PubMed PMID: 20716496.
  13. Liu X, Langer DL, Haider MA, Yang Y, Wernick MN, Yetik IS. Prostate cancer segmentation with simultaneous estimation of Markov random field parameters and class. IEEE Trans Med Imaging. 2009;28:906-15. doi.org/10.1109/TMI.2009.2012888. PubMed PMID: 19164079.
  14. Guo Y, Ruan S, Walker P, Feng Y, editors . Prostate cancer segmentation from multiparametric MRI based on fuzzy Bayesian model. 29 April-2 May 2014. Beijing: Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on; 2014.
  15. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26:1045-57. doi.org/10.1007/s10278-013-9622-7. PubMed PMID: 23884657. PubMed PMCID: 3824915.
  16. Lu W, Yao J, Lu C, Prindiville S, Chow C, editors. DCE-MRI segmentation and motion correction based on active contour model and forward mapping. 19-20 June 2006. Las Vegas: Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2006 SNPD 2006 Seventh ACIS International Conference on; 2006.
  17. Maintz JA, Viergever MA. A survey of medical image registration. Medical image analysis. 1998;2:1-36. doi.org/10.1016/S1361-8415(01)80026-8.
  18. Chen CJ, Chang RF, Moon WK, Chen DR, Wu HK. 2-D ultrasound strain images for breast cancer diagnosis using nonrigid subregion registration. Ultrasound Med Biol. 2006;32:837-46. doi.org/10.1016/j.ultrasmedbio.2006.02.1406. PubMed PMID: 16785006.
  19. Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9:671-5. doi.org/10.1038/nmeth.2089. PubMed PMID: 22930834.
  20. Padhani AR. Dynamic contrast-enhanced MRI studies in human tumours. Br J Radiol. 1999;72:427-31. doi.org/10.1259/bjr.72.857.10505003. PubMed PMID: 10505003.
  21. Alonzi R, Padhani AR, Allen C. Dynamic contrast enhanced MRI in prostate cancer. Eur J Radiol. 2007;63:335-50. doi.org/10.1016/j.ejrad.2007.06.028. PubMed PMID: 17689907.
  22. Navaei-Lavasani S, Fathi-Kazerooni A, Saligheh-Rad H, Gity M. Discrimination of Benign and Malignant Suspicious Breast Tumors Based on Semi-Quantitative DCE-MRI Parameters Employing Support Vector Machine. Frontiers in Biomedical Technologies. 2015;2:87-92.
  23. Fotouhi A, Eqlimi E, Makkiabadi B, editors. Evaluation of adaptive parafac alogorithms for tracking of simulated moving brain sources. 25-29 Aug. 2015. Milan: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE; 2015.
  24. Eqlimi E. Resting State Functional Connectivity Analysis Based on Mutual Information Graphs For MS Patients. 2013.
  25. Kuhl CK, Mielcareck P, Klaschik S, Leutner C, Wardelmann E, Gieseke J, et al. Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology. 1999;211:101-10. doi.org/10.1148/radiology.211.1.r99ap38101. PubMed PMID: 10189459.
  26. Isebaert S, De Keyzer F, Haustermans K, Lerut E, Roskams T, Roebben I, et al. Evaluation of semi-quantitative dynamic contrast-enhanced MRI parameters for prostate cancer in correlation to whole-mount histopathology. Eur J Radiol. 2012;81:e217-22. doi.org/10.1016/j.ejrad.2011.01.107. PubMed PMID: 21349667.
  27. Navaei Lavasani S, Fathi Kazerooni A, Saligheh Rad H, Gity M. Discrimination of Benign and Malignant Suspicious Breast Tumors Based on Semi-Quantitative DCE-MRI Parameters Employing Support Vector Machine. Frontiers in Biomedical Technologies. 2015;2:87-92.
  28. Unser M, Aldroubi A. A review of wavelets in biomedical applications. Proceedings of the IEEE. 1996;84:626-38. doi.org/10.1109/5.488704.
  29. Mallat S. A wavelet tour of signal processing: Academic press. Cambridge: Academic press; 1999.
  30. Poularikas AD. Transforms and applications handbook. Florida: CRC press; 2010.
  31. Struzik ZR, Siebes A, editors. The Haar wavelet transform in the time series similarity paradigm. European Conference on Principles of Data Mining and Knowledge Discovery: Springer; 1999.
  32. Birgani PM, Ashtiyani M, Asadi S, editors. MRI segmentation using fuzzy c-means clustering algorithm basis neural network. 7-11 April 2008. Damascus: Information and Communication Technologies: From Theory to Applications, 2008. ICTTA 2008. 3rd International Conference on; 2008.
  33. Ashtiyani M, Asadi S, Birgani PM, editors . ICA-based EEG classification using fuzzy c-mean algorithm. 7-11 April 2008. Damascus. Information and Communication Technologies: From Theory to Applications, 2008 ICTTA 2008 3rd International Conference on; 2008.
  34. Ashtiyani M, Behbahani S, Asadi S, Birgani PM, editors . Transmitting encrypted data by wavelet transform and neural network. 15-18 Dec. 2007. Giza: Signal Processing and Information Technology, 2007 IEEE International Symposium on; 2007.
  35. Mansoory MS, Ashtiyani M, Sarabadani H. Automatic crack detection in eggshell based on SUSAN Edge Detector using Fuzzy Thresholding. Modern Applied Science. 2011;5:117. doi.org/10.5539/mas.v5n6p117.
  36. Bezdek JC, Ehrlich R, Full W. FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences. 1984;10:191-203. doi.org/10.1016/0098-3004(84)90020-7.
  37. Kohavi R, John GH. Wrappers for feature subset selection. Artificial intelligence. 1997;97:273-324. doi.org/10.1016/S0004-3702(97)00043-X.
  38. Jamshidi O, Pilevar AH. Automatic segmentation of medical images using fuzzy c-means and the genetic algorithm. Journal of Computational Medicine. 2013;2013.
  39. Udupa JK, Leblanc VR, Zhuge Y, Imielinska C, Schmidt H, Currie LM, et al. A framework for evaluating image segmentation algorithms. Comput Med Imaging Graph. 2006;30:75-87. doi.org/10.1016/j.compmedimag.2005.12.001. PubMed PMID: 16584976.
  40. Medved M, Karczmar G, Yang C, Dignam J, Gajewski TF, Kindler H, et al. Semiquantitative analysis of dynamic contrast enhanced MRI in cancer patients: Variability and changes in tumor tissue over time. J Magn Reson Imaging. 2004;20:122-8. doi.org/10.1002/jmri.20061. PubMed PMID: 15221817.
  41. Jackson A, Reinsberg S, Sohaib S, Charles-Edwards E, Jhavar S, Christmas T, et al. Dynamic contrast-enhanced MRI for prostate cancer localization. The British journal of radiology. 2014.
Journal of Biomedical Physics and Engineering
Volume 8, Issue 1 - Serial Number 1
27
March 2018
Pages 107-116
Files
Share
How to cite
Statistics