Ipsilateral-mammogram computer-aided detection of breast cancer.

In this paper, an ipsilateral multi-view computer-aided detection (CAD) scheme is presented for mass detection in digital mammograms by exploiting correlative information of suspicious lesions between mammograms of the same breast. After nonlinear tree-structured filtering for image noise suppression, two wavelet-based methods, directional wavelet transform and tree-structured wavelet transform for image enhancement, and adaptive fuzzy C-means algorithm for segmentation are employed on each mammograms of the same breast, respectively, concurrent analysis is developed for iterative analysis of ipsilateral multi-view mammograms by inter-projective feature matching analysis. A supervised artificial neural network is developed as a classifier, in which the back-propagation algorithm combined with Kalman filtering is used as training algorithm, and free-response receiver operating characteristic analysis is used to test the performance of the developed unilateral CAD system. Performance comparison has been conducted between the final ipsilateral multi-view CAD system and our previously developed single-mammogram-based CAD system. The study results demonstrate the advantages of ipsilateral multi-view CAD method combined with concurrent analysis over current single-view CAD system on false positive reduction.

[1]  Dansheng Song,et al.  Digital mammography: hybrid M-channel wavelet transform for microcalcification segmentation , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  D P Chakraborty,et al.  Maximum likelihood analysis of free-response receiver operating characteristic (FROC) data. , 1989, Medical physics.

[3]  N. Wald,et al.  UKCCCR multicentre randomised controlled trial of one and two view mammography in breast cancer screening , 1995, BMJ.

[4]  Vijay K. Jain,et al.  Markov random field for tumor detection in digital mammography , 1995, IEEE Trans. Medical Imaging.

[5]  Craig A. Beam,et al.  Variability in the interpretation of screening mammograms by US radiologists. Findings from a national sample. , 1996, Archives of internal medicine.

[6]  L P Clarke,et al.  Digital mammography: computer-assisted diagnosis method for mass detection with multiorientation and multiresolution wavelet transforms. , 1997, Academic radiology.

[7]  R. Bird,et al.  Analysis of cancers missed at screening mammography. , 1992, Radiology.

[8]  Wei Qian,et al.  Image feature extraction for mass detection in digital mammography: Influence of wavelet analysis , 1999 .

[9]  Wei Qian,et al.  Multiresolution/multiorientation based nonlinear filters for image enhancement and detection in digital mammography , 2002 .

[10]  Nicholas Ayache,et al.  Medical Image Analysis: Progress over Two Decades and the Challenges Ahead , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  K Doi,et al.  Computerized detection of masses in digital mammograms: automated alignment of breast images and its effect on bilateral-subtraction technique. , 1994, Medical physics.

[12]  Eva Negri,et al.  Food groups and laryngeal cancer risk: A case‐control study from Italy and Switzerland , 2002, International journal of cancer.

[13]  L P Clarke,et al.  Digital mammography: hybrid four-channel wavelet transform for microcalcification segmentation. , 1998, Academic radiology.

[14]  Nico Karssemeijer,et al.  Detection of Masses in Mammograms , 2002 .

[15]  L. Liberman,et al.  Breast imaging reporting and data system (BI-RADS). , 2002, Radiologic clinics of North America.

[16]  Robert M. Nishikawa,et al.  Prospective Testing of a Clinical Mammography Workstation for CAD: Analysis of the First 10, 000 Cases , 1998, Digital Mammography / IWDM.

[17]  Baoyu Zheng,et al.  Digital mammography: mixed feature neural network with spectral entropy decision for detection of microcalcifications , 1996, IEEE Trans. Medical Imaging.

[18]  N. Petrick,et al.  Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study. , 1999, Radiology.

[19]  H P Chan,et al.  Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification. , 1996, Medical physics.

[20]  Dansheng Song,et al.  Ipsilateral multi-view CAD system for mass detection in digital mammography , 2001, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001).

[21]  Wei Qian,et al.  Tree-structured nonlinear filter and wavelet transform for microcalcification segmentation in mammography , 1993, Electronic Imaging.

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

[23]  T K Lau,et al.  Automated detection of breast tumors using the asymmetry approach. , 1991, Computers and biomedical research, an international journal.

[24]  W Qian,et al.  Digital mammography: wavelet transform and Kalman-filtering neural network in mass segmentation and detection. , 2001, Academic radiology.

[25]  David Gur,et al.  Multi-image CAD employing features derived from ipsilateral mammographic views , 1999, Medical Imaging.

[26]  Wei Qian,et al.  Tree-structured nonlinear filters in digital mammography , 1994, IEEE Trans. Medical Imaging.