Online citations, reference lists, and bibliographies.
← Back to Search

Assessment Of Feasibility To Use Computer Aided Texture Analysis Based Tool For Parametric Images Of Suspicious Lesions In DCE-MR Mammography

M. C. Kale, John David Fleig, Nazim Imal
Published 2013 · Computer Science, Medicine

Cite This
Download PDF
Analyze on Scholarcy
Share
Our aim was to analyze the feasibility of computer aided malignant tumor detection using the traditional texture analysis applied on two-compartment-based parameter pseudoimages of dynamic contrast-enhanced magnetic resonance (DCE-MR) breast image data. A major contribution of this research will be the through-plane assessment capability. Texture analysis was performed on two-compartment-based pseudo images of DCE-MRI datasets of breast data of eight subjects. The resulting texture parameter pseudo images were inputted to a feedforward neural network classification system which uses the manual segmentations of a primary radiologist as a gold standard, and each voxel was assigned as malignant or nonmalignant. The classification results were compared with the lesions manually segmented by a second radiologist. Results show that the mean true positive fraction (TPF) and false positive fraction (FPF) performance of the classifier vs. primary radiologist is statistically as good as the mean TPF and FPF performance of the second radiologist vs. primary radiologist with a confidence interval of 95% using a one-sample t-test with α = 0.05. In the experiment implemented on all of the eight subjects, all malignant tumors marked by the primary radiologist were classified to be malignant by the computer classifier. Our results have shown that neural network classification using the textural parameters for automated screening of two-compartment-based parameter pseudo images of DCE-MRI as input data can be a supportive tool for the radiologists in the preassessment stage to show the possible cancerous regions and in the postassessment stage to review the segmentations especially in analyzing complex DCE-MRI cases.
This paper references
Three-dimensional texture analysis of MRI brain datasets
V. Kovalev (2001)
10.1016/0730-725X(93)90205-R
MR image texture analysis--an approach to tissue characterization.
R. Lerski (1993)
10.1109/83.748890
Texture anisotropy in 3-D images
V. Kovalev (1999)
10.1097/00002142-200108000-00006
Dynamic contrast-enhanced magnetic resonance imaging in oncology.
M. Knopp (2001)
10.1002/(SICI)1522-2586(199909)10:3<260::AID-JMRI6>3.0.CO;2-7
Pathophysiologic basis of contrast enhancement in breast tumors
M. Knopp (1999)
10.1109/MEMB.2004.1360410
Dynamic magnetic resonance imaging of tumor perfusion
D. Collins (2004)
10.1002/mrm.10496
Textural analysis of contrast‐enhanced MR images of the breast
P. Gibbs (2003)
Classification of signal-time curves from dynamicMRmammography by neural networks,”Magnetic
R.E.A. Lucht (2001)
Neural network approach to the segmentation and classification of dynamic magnetic resonance images of the breast: comparison with empiric and quantitative kinetic parameters.
B. Szabó (2004)
10.1007/s00330-004-2280-x
Application of artificial neural networks to the analysis of dynamic MR imaging features of the breast
B. Szabó (2004)
10.1016/S0730-725X(01)00222-3
Classification of signal-time curves from dynamic MR mammography by neural networks.
R. Lucht (2001)
10.1097/00004728-199107000-00018
Pharmacokinetic parameters in CNS Gd-DTPA enhanced MR imaging.
G. Brix (1991)
10.1097/01.rli.0000164788.73298.ae
Classification of Signal-Time Curves Obtained by Dynamic Magnetic Resonance Mammography: Statistical Comparison of Quantitative Methods
R. Lucht (2005)
10.1109/TSMC.1973.4309314
Textural Features for Image Classification
R. Haralick (1973)
10.1016/S0730-725X(02)00464-2
Neural network-based segmentation of dynamic MR mammographic images.
R. Lucht (2002)
10.1007/978-1-4899-3216-7
Image Processing, Analysis and Machine Vision
M. Sonka (1993)
10.21236/ada597230
Image processing
T. Huang (1971)
10.1002/jmri.10259
Breast cancer detection in gadolinium‐enhanced MR images by static region descriptors and neural networks
A. Tzacheva (2003)
10.1016/J.ACRA.2004.09.006
Neural network approach to the segmentation and classification of dynamic magnetic resonance images of the breast: Comparison with empiric and quantitative kinetic parameters1
B. Szabó (2004)
10.2307/2532371
Fundamentals of biostatistics
B. Rosner (1982)
Dynamic magnetic resonance imaging of tumor perfusion. Approaches and biomedical challenges.
D. Collins (2004)
10.1148/RADIOLOGY.211.1.R99AP38101
Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions?
C. Kuhl (1999)
10.1109/TNN.2001.925574
Neural and Adaptive Systems: Fundamentals Through Simulations
K. Chen (2001)
10.1109/CCGRID.2003.1199350
Image processing for the grid: a toolkit for building grid-enabled image processing applications
Shannon Hastings (2003)
10.1109/TPAMI.1980.4767008
A Theoretical Comparison of Texture Algorithms
R. Conners (1980)
10.1109/TMI.2005.854517
An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data
T. Twellmann (2005)
10.1007/BF03190297
A use of a neural network to evaluate contrast enhancement curves in breast magnetic resonance images
D. Vergnaghi (2010)
10.1117/12.653273
Histologic characterization of DCE-MRI breast tumors with dimensional data reduction
C. Varini (2006)
Fundamentals of Biostatistics, Thomson Learning, Duxbury, Mass
B. Rosner (2000)
10.1006/gmip.1996.0016
Multidimensional Co-occurrence Matrices for Object Recognition and Matching
V. Kovalev (1996)
10.1148/RADIOLOGY.213.2.R99NV49317
Journey toward computer-aided diagnosis: role of image texture analysis.
G. Tourassi (1999)



This paper is referenced by
Semantic Scholar Logo Some data provided by SemanticScholar