Template matching by means of correlation coefficient for detecting cancerous masses in mammograms

Machine Graphics and Vision

Volumn 16, Issue 3-4, 2007, Pages 329-345

  Bator, Marcin ^a   Nieniewski, Mariusz ^a  

^a INSTITUTE OF FUNDAMENTAL TECHNOLOGICAL RESEARCH   (Poland)

Author keywords

Cancerous mass detection; Mammography; MIAS; Multiresolution; Multiscale; Template matching

Indexed keywords

COMPUTER AIDED ANALYSIS; COMPUTER GRAPHICS; COMPUTER NETWORKS; CURVES (ROAD); IMAGE PROCESSING; IMAGING SYSTEMS; IMAGING TECHNIQUES; MICROFLUIDICS; OPTICAL DATA PROCESSING; SEMICONDUCTING CADMIUM TELLURIDE; TEMPLATE MATCHING;

BIO-MEDICAL IMAGE PROCESSING; CORRELATION COEFFICIENT (CC); DETECTION METHODS; GRAY LEVEL DISTRIBUTION; MULTI SCALING; MULTIRESOLUTION (MR); NOVEL METHODS; OPERATING CHARACTERISTICS; OPTIMAL CHOICE; REGIONS OF INTEREST (ROI);

CORRELATION METHODS;

EID: 45549083618     PISSN: 12300535     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (38)

1. Lai S. M., Li X., and Bischof W. F., On Techniques for Detecting Circumscribed Masses in Mammograms, IEEE Transactions on Medical Imaging, 8(4), pp. 377-386.
2. Brzakovic D., Muo X. M., and Brzakovic P., An Approach to Automated Detection of Tumors in Mammograms, IEEE Transactions on Medical Imaging, 9(3), pp. 233-241.
3. Kegelmeyer W. P., Pruneda J. M., Bourland P. D., et al., Computer-Aided Mammographic Screening for Spiculated Lesions, Radiology, 191, pp. 331-337.
4. Suckling J., Parker J., Dance D., et al., The Mammographic Images Analysis Society Digital Mammogram Database. Digital Mammography, Elsevier, pp. 375-378.
5. Chan H. P., Wei D., Helvie M. A., et al., Computer-Aided Classification of Mammographic Masses and Normal Tissue: Linear Discriminant Analysis in Texture Feature Space, Physics in Medicine and Biology, 40, pp. 857-876.
6. Wei D., Chan H. P., Helvie M. A., et al., Classification of Mass and Normal Breast Tissue on Digital Mammograms: Multiresolution Texture Analysis, Medical Physics, 22(9), pp. 1501-1513.
7. Groshong B. R. and Kegelmeyer W. P., Evaluation of a Hough Transform Method for Circumscribed Lesion Detection. In: Digital Mammography '96, Doi et al. (Editors), Elsevier, pp. 361-366.
8. Karssemeijer W. P. and te Brake G. M., Detection of Stellate Distortions in Mammograms, IEEE Transactions on Medical Imaging, 15(5), pp. 611-619.
9. Matsubara T., Fujita H., Endo T., et al., Development of Mass Detection Algorithm Based on Adaptive Thresholding Technique in Digital Mammograms. In: Digital Mammography 96, Doi et al. (Editors), Elsevier, pp. 391-396.
10. Petrick H., Chan H. P., Sahiner B., et al., An Adaptive Density-Weighted Contrast Enhancement Filter for Mammographic Breast Mass Detection, IEEE Transactions on Medical Imaging, 15(1), pp 59-67.
11. Sahiner B., Chan H. P., Petrick N. et al., Classification of Mass and Normal Breast Tissue: A Convolution Neural Network Classifier with Spatial Domain and Texture Images, IEEE Transactions on Medical Imaging, 15(5), pp. 598-610.
12. Kupinski M. A. and Giger M. L., Investigation of Regularized Neural Networks for the Computerized Detection of Mass Lesions in Digital Mammograms, Proc. 19th Annual Int. Conference of the IEEE on Engineering in Medicine and Biology, v. 3, pp. 1336-1339.
13. Polakowski W. E., Cournoyer D. A. Rogers S. K., et al., Computer-Aided Breast Cancer Detection and Diagnosis of Masses Using Difference of Gaussians and Derivative-Based Feature Saliency, IEEE Transactions on Medical Imaging, 16(6), pp. 811-819.
14. Rangayyan R. M., el-Faramawy N. M., Desaultels J. E., et al., Measures of Acutance and Shape for Classification of Breast Tumors, IEEE Transactions on Medical Imaging, 16(6), pp. 799-810.
15. Wei D., Chan H. P., Petrick N., et al., False Positive Reduction Technique for Detection of Masses on Digital Mammograms: Global and Multiresolution Texture Analysis, Medical Physics, 24(6), pp. 903-914.
16. Kopans D. B., Breast Imaging, Lippincott-Raven Publishers, Philadelphia.
17. Sahiner B., Chan H. P., Petrick N., et al., Computerized Characterization of Masses on Mammograms: The Rubber Band Straightening Transform and Texture Analysis, Medical Physics, 25(4), pp. 516-526.
18. Bruce L. M. and Adhami R. R., Classifying Mammographic Mass Shapes Using the Wavelet Transform Modulus-Maxima Method, IEEE Transactions on Medical Imaging, 18(12), pp. 1170-1177.
19. Kobatake H., Murakami M., Takeo H., et al., Computerized Detection of Malignant Tumors on Digital Mammograms, IEEE Transactions on Medical Imaging, 18(5), pp. 369-378.
20. Qian W., Li L., Clarke L., et al., Comparison of Adaptive and Non Adaptive CAD Methods for Mass Detection, Academic Radiology, 6(8), pp. 471-480.
21. te Brake G. M. and Karssemeijer N., Single and Multiscale Detection of Masses in Digital Mammograms, IEEE Transactions on Medical Imaging, 18(7), pp. 628-639.
22. Mudigonda N. R., Rangayyan R. M., and Desautels J. E., Gradient and Texture Analysis for the Classification of Mammographic Masses, IEEE Transactions on Medical Imaging, 19(10), pp. 1032-1043.
23. te Brake G. M., Karssemeijer N., and Hendriks J. H., An Automatic Method to Discriminate Malignant Masses from Normal Tissue in Digital Mammograms, Physics in Medicine and Biology, 45(10), pp. 2843-2857.
24. Li H., Wang Y., Liu K. J. R., Lo S. B., and Preedman M. T., Computerized Radiographic Mass Detection - Part I: Lesion Site Selection by Morphological Enhancement and Contextual Segmentation, - Part II: Decision Support by Featured Database Visualization and Modular Neural Networks, IEEE Transactions on Medical Imaging, 20(4), pp. 289-301 and 302-313.
25. Liu S., Babbs C. F., and Delp E. J., Multiresolution Detection of Spiculated Lesions in Digital Mammograms, IEEE Transactions on Image Processing, 10(6), pp. 874-884.
26. Mudigonda N. R., Rangayyan R. M., and Desautels J. E. L., Detection of Breast Masses in Mammograms by Density Slicing and Texture Flow-Field Analysis, IEEE Transactions on Medical Imaging, 20(12), pp. 1215-1227.
27. Zheng L. and Chan A. K., An Artificial Intelligent Algorithm for Tumor Detection in Screening Mammogram, IEEE Transactions on Medical Imaging, 20(7), pp. 559-567.
28. Baydush A. H., Catarious D. M., Abbey C. K., et al., Computer-Assisted Detection of Masses in Mammography Using Subregion Hotelling Observers, Medical Physics, 30(7), pp. 1781-1787.
29. Tourassi G. D., Vaergas-Voracek R., Catarious D. M. et al., Computer-Assisted Detection of Mammographic Masses: A Template Matching Scheme Based on Mutual Information, Medical Physics, 30(8), pp. 2123-2130.
30. Timp S. and Karssemeijer N., A New Segmentation Method Based on Dynamic Programming Applied to Computer Aided Detection in Mammography, Medical Physics, 31(5), pp. 958-971.
31. Sampat M. P., Markey M. K., and Bovik A. C., Computer-Aided Detection and Diagnosis in Mammography. Handbook of Image and Video Processing, A. C. Bovik (Editor), Academic Press, pp. 1195-1217.
32. Thangavel K., Karnan M., Sivakumar R., et al., Automatic Detection of Microcalcifications in Mammograms - A Review, CAD System for Preprocessing and Enhancement of Digital Mammograms. ICGST International Journal on Graphics, Vision and Image Processing, 05(5), pp. 31-61.
33. th Conf. Medical Informatics & Technology, Malinka. pp. 324-329.
34. Roffilli M., Advanced Machine Learning Techniques for Digital Mammography, Technical Report UBLCS-2006-12, University of Bologna, Bologna.
35. Bator M., Ustymowicz M., and Nieniewski M., Experiences with Detection of Microcalcifications and Cancereous Masses in Mammograms. Chapter in "Intelligent Extraction of Information for Diagnostic Purposes" (a book in Polish), PWNT, Gdansk, pp. 305-322.
36. Doi K., Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential, Computerized Medical Imaging and Graphics, 31, pp. 198-211.
37. Kom G., Tiedeu A., and Kom M., Automated Detection of Masses in Mammograms by Local Adaptive Thresholding, Computers in Biology and Medicine, 37(1), pp. 37-48.
38. Nishikawa R. M., Current Status and Future Directions of Computer-Aided Diagnosis in Mammography, Computerized Medical Imaging and Graphics, 31, pp. 224-235.