TY - JOUR
T1 - Computer-aided diagnosis in dynamic contrast-enhanced magnetic resonance imaging of malignant tumor
T2 - a technical review of current research
AU - Zhou, Yuxiang
AU - Qin, Jing
AU - Bin, Guo
AU - Chen, Hanwei
AU - Feng, Shiting
AU - Wang, Tianfu
AU - Huang, Bingsheng
N1 - Publisher Copyright:
© 2016, Editorial Office of Journal of Biomedical Engineering. All right reserved.
PY - 2016/8/1
Y1 - 2016/8/1
N2 - Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may provide more information in diagnosis of malignant tumor compared to conventional magnetic resonance imaging (MRI). Nowadays, in order to utilize the information expediently and efficiently, many researchers are aiming at the development of computer-aided diagnosis (CAD) of malignant tumor based on DCE-MRI. In this review, we survey the research in this field and summarize the literature in four parts, i.e. (1) image preprocessing-noise reduction and image registration; (2) region of interests (ROI) segmentation; (3) feature extraction-exploring the image characteristics by analyzing the ROI quantitatively; (4) tumor lesion recognition and classification-distinguishing and classifying tumor lesions by learning the features of ROI. We summarize the application of CAD techniques of DCE-MRI for cancer diagnosis and, finally, give some discussion on how to improve the efficiency of CAD in the future research.
AB - Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may provide more information in diagnosis of malignant tumor compared to conventional magnetic resonance imaging (MRI). Nowadays, in order to utilize the information expediently and efficiently, many researchers are aiming at the development of computer-aided diagnosis (CAD) of malignant tumor based on DCE-MRI. In this review, we survey the research in this field and summarize the literature in four parts, i.e. (1) image preprocessing-noise reduction and image registration; (2) region of interests (ROI) segmentation; (3) feature extraction-exploring the image characteristics by analyzing the ROI quantitatively; (4) tumor lesion recognition and classification-distinguishing and classifying tumor lesions by learning the features of ROI. We summarize the application of CAD techniques of DCE-MRI for cancer diagnosis and, finally, give some discussion on how to improve the efficiency of CAD in the future research.
KW - Computer-aided diagnosis
KW - Dynamic contrast-enhanced magnetic resonance imaging
KW - Malignant tumor
UR - http://www.scopus.com/inward/record.url?scp=85043548474&partnerID=8YFLogxK
U2 - 10.7507/1001-5515.20160128
DO - 10.7507/1001-5515.20160128
M3 - Review article
C2 - 29714922
AN - SCOPUS:85043548474
SN - 1001-5515
VL - 33
SP - 794
EP - 800
JO - Shengwu Yixue Gongchengxue Zazhi/Journal of Biomedical Engineering
JF - Shengwu Yixue Gongchengxue Zazhi/Journal of Biomedical Engineering
IS - 4
ER -
