Automatic classification of clustered microcalcifications by a multiple classifier system

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2096, Issue , 2001, Pages 208-217

  Foggia, P ^a   Sansone, C ^a   Tortorella, F ^b   Vento, M ^a  

^a UNIVERSITY OF NAPLES FEDERICO II   (Italy)

^b UNIVERSITY OF CASSINO AND SOUTHERN LAZIO   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

DISEASES; MAMMOGRAPHY; X RAY SCREENS;

AUTOMATIC CLASSIFICATION; AUTOMATIC DETECTION; CLUSTERED MICROCALCIFICATIONS; DIAGNOSTIC TECHNIQUES; EARLY DETECTION OF BREAST CANCER; MAMMOGRAPHIC IMAGES; MICROCALCIFICATIONS; MULTIPLE CLASSIFIER SYSTEMS;

CALCIFICATION (BIOCHEMISTRY);

EID: 27544465617     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-48219-9_21     Document Type: Conference Paper

References (13)

1. [1] W.A. Murphy, K. DeSchryver-Kecskemeti, “Isolated clustered microcalcifications in the breast: radiologic-pathologic correlation”, Radiology, vol. 127, pp. 335-341, 1978.
2. [2] M. Lanyi, Diagnosis and differential diagnosis of breast calcifications, Springer-Verlag, New York, 1986.
3. [3] B. de Lafontan, J.P. Daures, B. Salicru, F. Eynius, J. Mihura, P. Rouanet, J.L. Lamarque, A. Naja, H. Pujol, “Isolated Clustered Microcalcifications: Diagnostic Value of Mammography—Series of 400 Cases with Surgical Verification”, Radiology, vol. 190, pp. 479-483, 1994.
4. [4] E.D. Pisano, F. Shtern “Image processing and Computer Aided Diagnosis in digital mammography: A clinical perspective”, Int. Journal of Pattern Recognition and Artificial Intelligence, vol. 7, no. 6, pp. 1493-1503, 1993.
5. [5] A.P. Dhawan, Y. Chitre, C. Kaiser-Bonasso, M. Moskowitz, “Analysis of Mammographic Microcalcifications Using Gray-Level Image Structure Features”, IEEE Trans. on Medical Imaging, vol. 15, no. 3, pp. 246-259, 1996.
6. [6] M.J. Lado, P.G. Tahoces, A.J. Méndez, M. Souto, J.J. Vidal, “A Wavelet-based Algorithm for Detecting Clustered Microcalcifications in Digital Mammograms”, Medical Physics, vol. 26, no. 7, pp. 1294-1305, 1999.
7. [7] S.S. Buchbinder, I.S. Leichter, P.N. Bamberger, B. Novak, R. Lederman, S. Fields, D.J. Behar, “Analysis of Clustered Microcalcifications by Using a Single Numeric Classifier Extracted from Mammographic Digital Images”, Academic Radiology, vol. 5, no. 11, pp. 779-784, 1998.
8. [8] T.K Ho, J.J. Hull, S. Shrihari, “Decision Combination in Multiple Classifier Systems”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol 16, no.1, pp.66-75, 1994.
9. [9] J. Kittler, “A Framework for Classifier Fusion: Is It Still Needed ?”, in F.J. Ferri, J.M. Iñesta, A. Amin, P. Pudil (eds.), Advances in Pattern Recognition, Lecture Notes in Computer Science 1876, pp. 45-56, Springer, Berlin, 2000.
10. [10] L. Shen, R.M. Rangayyan, J.E.L. Desautels, “Application of Shape Analysis to Mammographic Calcifications”, IEEE Trans. on Medical Imaging, vol. 13, no. 2, pp. 234-253, 1994.
11. [11] N. Karssemeijer, “Adaptive Noise Equalization and Recognition of Microcalcification Clusters in Mammograms”, Int. Journal of Pattern Recognition and Artificial Intelligence, vol. 7, no. 6, pp. 1357-1376, 1993.
12. [12] C.E. Metz, “ROC methodology in radiologic imaging”, Investigative Radiology, vol. 21, pp. 720-733, 1986.
13. [13] C.E. Metz, B.A. Herman, J-H. Shen, “Maximum-likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data”, Statistics in Medicine, vol. 17, no. 9, pp. 1033-53.