Malignant‐lesion segmentation using 4D co‐occurrence texture analysis applied to dynamic contrast‐enhanced magnetic resonance breast image data

To investigate the use of four‐dimensional (4D) co‐occurrence‐based texture analysis to distinguish between nonmalignant and malignant tissues in dynamic contrast‐enhanced (DCE) MR images.

[1]  L. Turnbull,et al.  Textural analysis of contrast‐enhanced MR images of the breast , 2003, Magnetic resonance in medicine.

[2]  David J Collins,et al.  Dynamic magnetic resonance imaging of tumor perfusion. Approaches and biomedical challenges. , 2004, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[3]  R. Lucht,et al.  Neural network-based segmentation of dynamic MR mammographic images. , 2002, Magnetic resonance imaging.

[4]  Kayvan Najarian,et al.  Breast cancer detection in gadolinium‐enhanced MR images by static region descriptors and neural networks , 2003, Journal of magnetic resonance imaging : JMRI.

[5]  W S Kerwin,et al.  Noise and motion correction in dynamic contrast‐enhanced MRI for analysis of atherosclerotic lesions , 2002, Magnetic resonance in medicine.

[6]  S. Feig Breast masses. Mammographic and sonographic evaluation. , 1992, Radiologic clinics of North America.

[7]  G Brix,et al.  Elastic matching of dynamic MR mammographic images , 2000, Magnetic resonance in medicine.

[8]  Tim W. Nattkemper,et al.  An Adaptive Tissue Characterisation Network for Model-Free Visualisation of Dynamic Contrast-Enhanced Magnetic Resonance Image Data , 2005 .

[9]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[10]  M. Noguchi,et al.  Breast cancer in dense breast: Detection with contrast‐enhanced dynamic MR imaging , 2000, Journal of magnetic resonance imaging : JMRI.

[11]  G. Tourassi Journey toward computer-aided diagnosis: role of image texture analysis. , 1999, Radiology.

[12]  W E Reddick,et al.  MR imaging of tumor microcirculation: Promise for the new millenium , 1999, Journal of magnetic resonance imaging : JMRI.

[13]  L R Schad,et al.  Pharmacokinetic parameters in CNS Gd-DTPA enhanced MR imaging. , 1991, Journal of computer assisted tomography.

[14]  J. Gomori,et al.  Breast fibroadenoma: mapping of pathophysiologic features with three-time-point, contrast-enhanced MR imaging--pilot study. , 1999, Radiology.

[15]  S. Rankin,et al.  MRI of the breast. , 2000, The British journal of radiology.

[16]  A. Berger FUNDAMENTALS OF BIOSTATISTICS , 1969 .

[17]  G Brix,et al.  Classification of signal-time curves from dynamic MR mammography by neural networks. , 2001, Magnetic resonance imaging.

[18]  D. Collins,et al.  Dynamic magnetic resonance imaging of tumor perfusion , 2004, IEEE Engineering in Medicine and Biology Magazine.

[19]  Richard W. Conners,et al.  A Theoretical Comparison of Texture Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  C. Kuhl,et al.  Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? , 1999, Radiology.

[21]  Joel H. Saltz,et al.  A Parallel Implementation of 4-Dimensional Haralick Texture Analysis for Disk-Resident Image Datasets , 2004, Proceedings of the ACM/IEEE SC2004 Conference.

[22]  L. Schad,et al.  MR image texture analysis--an approach to tissue characterization. , 1993, Magnetic resonance imaging.