Automated detection and diagnosis of small lesions in breast MRI represents a challenge for the traditional computer-aided diagnosis (CAD) systems. The goal of the present research was to compare and determine the optimal feature sets describing the morphology and the enhancement kinetic features for a set of small lesions and to determine their diagnostic performance. For each of the small lesions, we extracted morphological and dynamical features describing both global and local shape, and kinetics behavior. In this paper, we compare the performance of each extracted feature set for the differential diagnosis of enhancing lesions in breast MRI. Based on several simulation results, we determined the optimal feature number and tested different classification techniques. The results suggest that the computerized analysis system based on spatiotemporal features has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography. 1. Introduction Breastcancer is one of the most common cancers among women. Contrast-enhanced MR imaging of the breast was reported to be a highly sensitive method for the detection of invasive breast cancer [1]. Different investigators described that certain dynamic signal intensity (SI) characteristics (rapid and intense contrast enhancement followed by a wash out phase) obtained in dynamic studies are a strong indicator for malignancy [2]. Morphologic criteria have also been identified as valuable diagnostic tools [3]. Recently, combinations of different dynamic and morphologic characteristics have been reported [4] that can reach diagnostic sensitivities up to 97 and specificities up to 76.5 . As an important aspect remains the fact that many of these techniques were applied on a database of predominantly tumors of a size larger than 2？cm. In these cases, MRI reaches a very high sensitivity in the detection of invasive breast cancer due to both morphological criteria as well as characteristic time-signal intensity curves. However, the value of dynamic MRI and of automatic identification and classification of characteristic kinetic curves is not well established in small lesions when clinical findings, mammography, and ultrasound are unclear. Recent clinical research has shown that DCIS with small invasive carcinoma can be adequately visualized in MRI [5] and that MRI provides an accurate estimation of invasive breast cancer tumor size, especially in tumors of 2？cm or smaller [6]. Visual assessment of
References
[1]
S. G. Orel, M. D. Schnall, C. M. Powell et al., “Staging of suspected breast cancer: effect of MR imaging and MR-guided biopsy,” Radiology, vol. 196, no. 1, pp. 115–122, 1995.
[2]
C. K. Kuhl, P. Mielcareck, S. Klaschik et al., “Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions?” Radiology, vol. 211, no. 1, pp. 101–110, 1999.
[3]
M. D. Schnall, S. Rosten, S. Englander, S. G. Orel, and L. W. Nunes, “A combined architectural and kinetic interpretation model for breast MR images,” Academic Radiology, vol. 8, no. 7, pp. 591–597, 2001.
[4]
B. K. Szabó, P. Aspelin, M. Wiberg, and B. Bone, “Dynamic MR imaging of the breast. Analysis of kinetic and morphologic diagnostic criteria,” Acta Radiologica, vol. 44, no. 4, pp. 379–386, 2003.
[5]
A. P. Schouten van der Velden, C. Boetes, P. Bult, and T. Wobbes, “The value of magnetic resonance imaging in diagnosis and size assessment of in situ and small invasive breast carcinoma,” American Journal of Surgery, vol. 192, no. 2, pp. 172–178, 2006.
[6]
G. M. Grimsby, R. Gray, A. Dueck et al., “Is there concordance of invasive breast cancer pathologic tumor size with magnetic resonance imaging?” The American Journal of Surgery, vol. 198, no. 4, pp. 500–504, 2009.
[7]
M. J. Stoutjesdijk, J. J. Fütterer, C. Boetes, L. E. Van Die, G. Jager, and J. O. Barentsz, “Variability in the description of morphologic and contrast enhancement characteristics of breast lesions on magnetic resonance imaging,” Investigative Radiology, vol. 40, no. 6, pp. 355–362, 2005.
[8]
I. M. A. Obdeijn, C. E. Loo, A. J. Rijnsburger et al., “Assessment of false-negative cases of breast MR imaging in women with a familial or genetic predisposition,” Breast Cancer Research and Treatment, vol. 119, no. 2, pp. 399–407, 2010.
[9]
G. D. Tourassi, R. Vargas-Voracek, D. M. Catarious, and C. E. Floyd, “Computer-assisted detection of mammographic masses: a template matching scheme based on mutual information,” Medical Physics, vol. 30, no. 8, pp. 2123–2130, 2003.
[10]
G. D. Tourassi, B. Harrawood, S. Singh, and J. Y. Lo, “Information-theoretic CAD system in mammography: entropy-based indexing for computational efficiency and robust performance,” Medical Physics, vol. 34, no. 8, pp. 3193–3204, 2007.
[11]
G. D. Tourassi, R. Ike, S. Singh, and B. Harrawood, “Evaluating the effect of image preprocessing on an information-theoretic CAD system in mammography,” Academic Radiology, vol. 15, no. 5, pp. 626–634, 2008.
[12]
L. Hadjiiski, B. Sahiner, and H. Chan, “Evaluating the effect of image preprocessing on an information-theoretic cad system in mammography.,” Current Opinion in Obstetrics and Gynecology, vol. 18, no. 7, pp. 64–70, 2006.
[13]
M. A. Kupinski and M. L. Giger, “Automated seeded lesion segmentation on digital mammograms,” IEEE Transactions on Medical Imaging, vol. 17, no. 4, pp. 510–517, 1998.
[14]
S. Behrens, H. Laue, M. Althaus et al., “Computer assistance for MR based diagnosis of breast cancer: present and future challenges,” Computerized Medical Imaging and Graphics, vol. 31, no. 4-5, pp. 236–247, 2007.
[15]
A. Hill, A. Mehnert, S. Crozier, and K. McMahon, “Evaluating the accuracy and impact of registration in dynamic contrast-enhanced breast MRI,” Concepts in Magnetic Resonance B, vol. 35, no. 2, pp. 106–120, 2009.
[16]
N. Papenberg, A. Bruhn, T. Brox, S. Didas, and J. Weickert, “Highly accurate optic flow computation with theoretically justified warping,” International Journal of Computer Vision, vol. 67, no. 2, pp. 141–158, 2006.
[17]
B. Horn and B. Schunck, Determining optical flow, 1981.
[18]
F. Steinbruecker, A. Meyer-Baese, A. Wismueller, and T. Schlossbauer, “Application and evaluation of motion compensation technique to breast mri,” in Proceedings of the Evolutionary and Bio-Inspired Computation: Theory and Applications III, vol. 7347 of Proceedings of SPIE, pp. 73470J–73470J-8, 2009.
[19]
D. Xu and H. Li, “Geometric moment invariants,” Pattern Recognition, vol. 41, no. 1, pp. 240–249, 2008.
[20]
A. Mademlis, A. Axenopoulos, P. Daras, D. Tzovaras, and M. G. Strintzis, “3D content-based search based on 3D Krawtchouk moments,” in Proceedings of the 3rd International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT '06), pp. 743–749, June 2006.
[21]
P. T. Yap, R. Paramesran, and S. H. Ong, “Image analysis by Krawtchouk moments,” IEEE Transactions on Image Processing, vol. 12, no. 11, pp. 1367–1377, 2003.
[22]
F. Jamitzky, R. W. Stark, W. Bunk et al., “Scaling-index method as an image processing tool in scanning-probe microscopy,” Ultramicroscopy, vol. 86, no. 1-2, pp. 241–246, 2001.
[23]
S. Theodoridis and K. Koutroumbas, Pattern Recognition, Academic Press, 1998.
