Paper Information

Journal:   JOURNAL OF BIOMEDICAL PHYSICS AND ENGINEERING   2018 , Volume 8 , Number 1; Page(s) 107 To 116.
 
Paper: 

AUTOMATIC PROSTATE CANCER SEGMENTATION USING KINETIC ANALYSIS IN DYNAMIC CONTRAST-ENHANCED MRI

 
 
Author(s):  NAVAEI LAVASANI S., MOSTAAR A.*, ASHTIYANI M.
 
* 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 (DCEMRI) 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, speci?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.

 
Keyword(s): DCE-MRI, PROSTATE CANCER, SEMI-QUANTITATIVE FEATURE, WAVELET KINETIC FEATURE, SEGMENTATION
 
References: 
  • ندارد
 
  pdf-File tarjomyar Yearly Visit 73
 
Latest on Blog
Enter SID Blog