Automated analysis of single and joint kinetic and morphologic features for non-masses

The evaluation of kinetic and/or morphologic characteristics of non-masses represents a challenging task for an automated analysis and is of crucial importance for advancing current computer-aided diagnosis (CAD) systems. Compared to the well-characterized mass-enhancing lesions, non-masses have not well-dened and blurred tumor borders and a kinetic behavior that is not easily generalizable and thus discriminative for malignant and benign non-masses. To overcome these diculties and pave the way for novel CAD systems for non-masses, we will evaluate several kinetic and morphologic descriptors separately, and a novel technique, the Zernike velocity moments, to capture the joint spatio-temporal behavior of these lesions. We additionally consider the impact of non-rigid motion compensation on a correct diagnosis.

[1]  Hon J. Yu,et al.  Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement , 2009, European Radiology.

[2]  F. B. Fuller.  The writhing number of a space curve. , 1971, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[4]  Hiroshi Honda,et al.  Non-mass-like enhancement on contrast-enhanced breast MR imaging: lesion characterization using combination of dynamic contrast-enhanced and diffusion-weighted MR images. , 2010, European journal of radiology.

[5]  Michael G. Strintzis,et al.  3D Content-Based Search Based on 3D Krawtchouk Moments , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[6]  Thomas Brox,et al.  High Accuracy Optical Flow Estimation Based on a Theory for Warping , 2004, ECCV.

[7]  Xiaobing Fan,et al.  The diverse pathology and kinetics of mass, nonmass, and focus enhancement on MR imaging of the breast , 2011, Journal of magnetic resonance imaging : JMRI.

[8]  Heinz-Otto Peitgen,et al.  Computer assistance for MR based diagnosis of breast cancer: Present and future challenges , 2007, Comput. Medical Imaging Graph..

[9]  Raveendran Paramesran,et al.  Image analysis by Krawtchouk moments , 2003, IEEE Trans. Image Process..

[10]  Mark S. Nixon,et al.  Zernike velocity moments for sequence-based description of moving features , 2006, Image Vis. Comput..

[11]  Jong Hyo Kim,et al.  Multilevel analysis of spatiotemporal association features for differentiation of tumor enhancement patterns in breast DCE-MRI. , 2010, Medical physics.

[12]  Eric L. Miller,et al.  3D Shape Analysis of Intracranial Aneurysms Using the Writhe Number as a Discriminant for Rupture , 2011, Annals of Biomedical Engineering.

[13]  Hiroyuki Abe,et al.  DCEMRI of breast lesions: Is kinetic analysis equally effective for both mass and nonmass-like enhancement? , 2008, Medical physics.

[14]  M. Aizerman,et al.  Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .

[15]  Anke Meyer-Bäse,et al.  Evaluation and visual exploratory analysis of DCE-MRI Data of breast lesions based on morphological features and novel dimension reduction methods , 2009, 2009 International Joint Conference on Neural Networks.

[16]  Anke Meyer-Bäse,et al.  Improved computer-aided diagnosis for breast lesions detection in DCE-MRI based on image registration and integration of morphologic and dynamic characteristics , 2011, Defense + Commercial Sensing.

[17]  Werner A. Kaiser,et al.  Kinetic analysis of lesions without mass effect on breast MRI using manual and computer-assisted methods , 2011, European Radiology.

[18]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

[19]  Eric L. Miller,et al.  Automated detection of intracranial aneurysms based on parent vessel 3D analysis , 2010, Medical Image Anal..