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Navaei Lavasani, S, Mostaar, A, Ashtiyani, M. (1396). Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI. سامانه مدیریت نشریات علمی, 8(1), 107-116.
S Navaei Lavasani; A Mostaar; M Ashtiyani. "Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI". سامانه مدیریت نشریات علمی, 8, 1, 1396, 107-116.
Navaei Lavasani, S, Mostaar, A, Ashtiyani, M. (1396). 'Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI', سامانه مدیریت نشریات علمی, 8(1), pp. 107-116.
Navaei Lavasani, S, Mostaar, A, Ashtiyani, M. Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI. سامانه مدیریت نشریات علمی, 1396; 8(1): 107-116.

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

Journal of Biomedical Physics and Engineering
مقاله 11، دوره 8، شماره 1، خرداد 2018، صفحه 107-116    اصل مقاله (589.57 K)
نوع مقاله: Original Research
نویسندگان
S Navaei Lavasani؛ A Mostaar* ؛ M Ashtiyani
Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
چکیده
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
کلیدواژه‌ها
DCE-MRI؛ Prostate Cancer؛ Semi-quantitative Feature؛ Wavelet Kinetic Feature؛ Segmentation
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