Mammographic Image
Analysis Homepage

 

Most computer-aided detection and diagnosis algorithms in mammographic image analysis consist of few typical steps: segmentation, feature extraction, feature selection and classification. Here we give a brief description of each of these steps together with the most representative scientific papers published in high impact factor journals. One of the criteria for the selection of papers was their relevance and the number of citations according to SCOPUS database.

Segmentation | Feature Extraction | Feature Selection | Classification

SEGMENTATION

The aim of the segmentation step in mammographic image analysis is to extract regions of interest (ROIs) containing all breast abnormalities from the normal breast tissue. Another aim of the segmentation is to locate the suspicious lesion candidates from the region of interest.

H.D. Li, M. Kallergi, L.P. Clarke, V.K. Jain, R.A. Clark, Markov Random Field for Tumor Detection in Digital Mammography, IEEE Transactions on Medical Imaging, Vol. 14, No. 3, 1995, pp. 565-576
link

F. Lefebvre, H. Benali, R. Gilles, E. Kahn, R. Di Paola, A Fractal Approach to the Segmentation of Microcalcifications in Digital Mammograms, Medical Physics, Vol. 22, No. 4, 1995, pp. 381-390
link

W. Qian, M. Kallergi, L.P. Clarke, H.-D. Li, P. Venugopal, D. Song, R.A. Clark, Tree Structured Wavelet Transform Segmentation of Microcalcifications in Digital Mammography, Medical Physics, Vol. 22, No. 8, 1995, pp. 1247-1254
link

N. Patrick, H.-P. Chan, B. Sahiner, D. Wei, An Adaptive Density-Weighted Contrast Enhancement Filter for Mammographic Breast Mass Detection, IEEE Transactions on Medical Imaging, Vol. 15, No. 1, 1996, pp. 59-67
link

H. Li, K.J.R. Liu, S.-C.B. Lo, Fractal Modeling and Segmentation for the Enhancement of Microcalcifications in Digital Mammograms, IEEE Transactions on Medical Imaging, Vol. 16, No. 6, 1997, pp. 785-798
link

M.A. Kupinski, M.L. Giger, Automated Seeded Lesion Segmentation on Digital Mammograms, IEEE Transactions on Medical Imaging, Vol. 17, No. 4, 1998, pp. 510-517
link

N. Petrick, H.-P. Chan, B. Sahiner, M.A. Helvie, Combined Adaptive Enhancement and Region-Growing Segmentation of Breast Masses on Digitized Mammograms, Medical Physics, Vol. 26, No. 8, 1999, pp. 1642-1654
link

M.A. Gavrielides, J.Y. Lo, R. Vargas-Voracek, C.E. Floyd Jr., Segmentation of Suspicious Clustered Microcalcifications in Mammograms, Medical Physics, Vol. 27, No. 1, 2000, pp. 13-22
link

G.M. te Brake, N. Karssemeijer, Segmentation of Suspicious Densities in Digital Mammograms, Medical Physics, Vol. 28, No. 2, 2001, pp. 259-266
link

H. Li, Y. Wang, K.J.R. Liu, S.-C.B. Lo, M.T. Freedman, Computerized Radiographic Mass Detection - Part I: Lesion Site Selection by Morphological Enhancement and Contextual Segmentation, IEEE Transactions on Medical Imaging, Vol. 20, No. 4, 2001, pp. 289-301
link

A.R. Domínguez, A.K. Nandi, Improved Dynamic-Programming-Based Algorithms for Segmentation of Masses in Mammograms, Medical Physics, Vol. 34, No. 11, 2007, pp. 4256-4269
link

back to top

 


FEATURE EXTRACTION

In the feature extraction step of the mammographic image analysis algorithms the features are calculated from the characteristics of the region of interest.

C.J. D'Orsi, D.B. Kopans, Mammographic Feature Analysis, Seminars in Roentgenology, Vol. 28, No. 3, 1993, pp. 204-230
link

Z. Huo, M.L. Giger, C.J. Vyborny, U. Bick, P. Lu, D.E. Wolverton, R.A. Schmidt, Analysis of Spiculation in the Computerized Classification of Mammographic Masses, Medical Physics, Vol. 22, No. 10, 1995, pp. 1569-1579
link

A.P. Dhawan, Y. Chitre, C. Kaiser-Bonasso, M. Moskowitz, Analysis of Mammographic Microcalcifications Using Gray-Level Image Structure Features, IEEE Transactions on Medical Imaging, Vol. 15, No. 3, 1996, pp. 246-259
link

B. Sahiner, H.-P. Chan, N. Petrick, M.A. Helvie, M.M. Goodsitt, Computerized Characterization of Masses on Mammograms: the Rubber Band Straightening Transform and Texture Analysis, Medical Physics, Vol. 25, No. 4, 1998, pp. 516-526
link

H.-P. Chan, B. Sahiner, K.L. Lam, N. Petrick, M.A. Helvie, M.M. Goodsitt, D.D. Adler, Computerized Analysis of Mammographic Microcalcifications in Morphological and Texture Feature Spaces, Medical Physics, Vol. 25, No. 10, 1998, pp. 2007-2019
link

W. Qian, L. Li, L.P. Clarke, Image Feature Extraction for Mass Detection in Digital Mammography: Influence of Wavelet Analysis, Medical Physics, Vol. 26, No. 3, 1999, pp. 402-408
link

H. Kobatake, M. Murakami, H. Takeo, S. Nawano, Computerized Detection of Malignant Tumors on Digital Mammograms, IEEE Transactions on Medical Imaging, Vol. 18, No. 5, 1999, pp. 369-378
link

N.R. Mudigonda, R.M. Rangayyan, J.E.L. Desautels, Gradient and Texture Analysis for the Classification of Mammographic Masses, IEEE Transactions on Medical Imaging, Vol. 19, No. 10, 2000, pp. 1032-1043
link

B. Verma, J. Zakos, A Computer-Aided Diagnosis System for Digital Mammograms Based on Fuzzy-Neural and Feature Extraction Techniques, IEEE Transactions on Information Technology in Biomedicine, Vol. 5, No. 1, 2001, pp. 46-54
link

L. Hadjiiski, B. Sahiner, H.-P. Chan, N. Petrick, M.A. Helvie, M. Gurcan, Analysis of Temporal Changes of Mammographic Features: Computer-Aided Classification of Malignant and Benign Breast Masses, Medical Physics, Vol. 28, No. 11, 2001, pp. 2309-2317
link

S. Timp, N. Karssemeijer, Interval Change Analysis to Improve Computer Aided Detection in Mammography, Medical Image Analysis, Vol. 10, No. 1, 2006, pp. 82-95
link

back to top

 


FEATURE SELECTION

Some of the features extracted from the regions of interest in the mammographic image are not significant when observed alone, but in combination with other features they can be significant for classification. The best set of features for eliminating false positives and for classifying lesion types as benign or malignant are selected in the feature selection step.

B. Sahiner, H.-P. Chan, D. Wei, N. Petrick, M.A. Helvie, D.D. Adler, M.M. Goodsitt, Image Feature Selection by a Genetic Algorithm: Application to Classification of Mass and Normal Breast Tissue, Medical Physics, Vol. 23, No. 10, 1996, pp. 1671-1684
link

B. Zheng, Y.-H. Chang, X.-H. Wang, W.F. Good, D. Gur, Feature Selection for Computerized Mass Detection in Digitized Mammograms by Using a Genetic Algorithm, Academic Radiology, Vol. 6, No. 6, 1999, pp. 327-332
link

Z. Huo, M.L. Giger, D.E. Wolverton, W. Zhong, S. Cumming, O.I. Olopade, Computerized Analysis of Mammographic Parenchymal Patterns for Breast Cancer Risk Assessment: Feature Selection, Medical Physics, Vol. 27, No. 1, 2000, pp. 4-12
link

R.J. Nandi, A.K. Nandi, R.M. Rangayyan, D. Scutt, Classification of Breast Masses in Mammograms Using Genetic Programming and Feature Selection, Medical and Biological Engineering and Computing, Vol. 44, No. 8, 2006, pp. 683-694
link

B. Verma, P. Zhang, A Novel Neural-Genetic Algorithm to Find the Most Significant Combination of Features in Digital Mammograms, Applied Soft Computing Journal, Vol. 7, No. 2, 2007, pp. 612-625
link

back to top

 


CLASSIFICATION

In the classification step of the mammographic image analysis algorithms lesions are classified as benign or malignant on the basis of selected features.

Y. Wu, K. Doi, M.L. Giger, R.M. Nishikawa, Computerized Detection of Clustered Microcalcifications in Digital Mammograms: Applications of Artificial Neural Networks, Medical Physics, Vol. 19, No. 3, 1992, pp. 555-560
link

H.-P. Chan, D. Wei, M.A. Helvie, B. Sahiner, D.D. Adler, M.M. Goodsitt, N. Petrick, Computer-Aided Classification of Mammographic Masses and Normal Tissue: Linear Discriminant Analysis in Texture Feature Space, Physics in Medicine and Biology, Vol. 40, No. 5, 1995, pp. 857-876
link

H.-P. Chan, S.-C.B. Lo, B. Sahiner, Kwok Leung Lam, M.A. Helvie, Computer-Aided Detection of Mammographic Microcalcifications: Pattern Recognition with an Artificial Neural Network, Medical Physics, Vol. 22, No. 10, 1995, pp. 1555-1567
link

Y. Jiang, R.M. Nishikawa, D.E. Wolverton, C.E. Metz, M.L. Giger, R.A. Schmidt, C.J. Vyborny, K. Doi, Malignant and Benign Clustered Microcalcifications: Automated Feature Analysis and Classification, Radiology, Vol. 198, No. 3, 1996, pp. 671-678
link

B. Sahiner, H.-P. Chan, N. Petrick, D. Wei, M.A. Helvie, D.D. Adler, M.M. Goodsitt, Classification of Mass and Normal Breast Tissue: A Convolution Neural Network Classifier with Spatial Domain and Texture Images, IEEE Transactions on Medical Imaging, Vol. 15, No. 5, 1996, pp. 598-610
link

H.-P. Chan, B. Sahiner, N. Patrick, M.A. Helvie, K.L. Lam, D.D. Adler, M.M. Goodsitt, Computerized Classification of Malignant and Benign Microcalcifications on Mammograms: Texture Analysis Using an Artificial Neural Network, Physics in Medicine and Biology, Vol. 42, No. 3, 1997, pp. 549-567
link

Z. Huo, M.L. Giger, C.J. Vyborny, D.E. Wolverton, R.A. Schmidt, K. Doi, Automated Computerized Classification of Malignant and Benign Masses on Digitized Mammograms, Academic Radiology, Vol. 5, No. 3, 1998, pp. 155-168
link

W.J.H. Veldkamp, N. Karssemeijer, J.D.M. Otten, J.H.C.L. Hendriks, Automated Classification of Clustered Microcalcifications into Malignant and Benign Types, Medical Physics, Vol. 27, No. 11, 2000, pp. 2600-2608
link

A. Papadopoulos, D.I. Fotiadis, A. Likas, An Automatic Microcalcification Detection System Based on a Hybrid Neural Network Classifier, Artificial Intelligence in Medicine, Vol. 25, No. 2, 2002, pp. 149-167
link

L. Wei, Y. Yang, R.M. Nishikawa, Y. Jiang, A Study on Several Machine-Learning Methods for Classification of Malignant and Benign Clustered Microcalcifications, IEEE Transactions on Medical Imaging, Vol. 24, No. 2, 2005, pp. 371-380
link

P. Zhang, B. Verma, K. Kumar, Neural vs. Statistical Classifier in Conjunction with Genetic Algorithm Based Feature Selection, Pattern Recognition Letters, Vol. 26, No. 7, 2005, pp. 909-919
link

back to top

 


© 2008-2017 VCL
Last update: March 1, 2009
Back to Main Page