Use of border information in the classification of mammographic masses

Physics in Medicine and Biology

Volumn 51, Issue 2, 2006, Pages 425-441

  Varela, C ^a,b   Timp, S ^a   Karssemeijer, N ^a  

^a RADBOUD UNIVERSITY MEDICAL CENTER   (Netherlands)

^b UNIVERSITY OF SANTIAGO DE COMPOSTELA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER APPLICATIONS; IMAGE PROCESSING; NEURAL NETWORKS;

MAMMOGRAPHIC MASSES; MASS DETECTION;

MAMMOGRAPHY;

AREA UNDER THE CURVE; ARTICLE; ARTIFICIAL NEURAL NETWORK; BENIGN TUMOR; BREAST CANCER; BREAST TUMOR; CALCULATION; CANCER CLASSIFICATION; CANCER DIAGNOSIS; CANCER SCREENING; CLINICAL FEATURE; COMPUTER ASSISTED RADIOGRAPHY; CONTRAST; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC IMAGING; DIFFERENTIAL DIAGNOSIS; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MALIGNANT NEOPLASTIC DISEASE; MAMMOGRAPHY; PERFORMANCE; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; ROC CURVE;

ALGORITHMS; BREAST DISEASES; BREAST NEOPLASMS; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; MAMMOGRAPHY; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; ROC CURVE;

EID: 32544461460     PISSN: 00319155     EISSN: None     Source Type: Journal    
DOI: 10.1088/0031-9155/51/2/016     Document Type: Article

References (36)

1. American College of Radiology 2003 American College of Radiology Breast Imaging Reporting and Data System Atlas (BI-RADS Atlas) (Reston, VA: American College of Radiology (ACR))
2. Chan H, Sahiner B, Helvie M, Petrick N, Roubidoux M, Wilson T, Adler D, Paramagul C, Newman J and Sanjay-Gopal S 1999 Improvement of radiologists's characterization of mammographic masses by using computer-aided diagnosis: an roc study Radiology 212 817-27
3. Chu A, Sehgal C and Greenleaf J 1990 Use of gray level value distribution of run lengths for texture analysis Pattern Recognit. Lett. 11 415-20
4. Duda R and Hart P 1973 Pattern Classification and Scene Analysis (New York: Wiley)
5. Evans W 1995 Breast masses. Appropriate evaluation Radiol. Clin. N. Am. 33 1085-108
6. Galloway M 1975 Texture analysis using gray level run lengths Comput. Graph. Image Process. 4 172-9
7. Hall F, Storella J, Silverstone D and Wyshak G 1988 Nonpalpable breast lesions: recommendations for biopsy based on suspicion of carcinoma at mammography Radiology 167 353-8
8. Haralick R, Shanmugam K and Dinstein I 1973 Textural features for image classification IEEE Trans. Syst. Man Cybern. 3 610-21
9. Heath M, Bowyer K, Kopans D, Kegelmeyer P Jr, Moore R, Chang K and Munishkumaran S 1998 Current status of the digital database for screening mammography Digital Mammography (Dordrecht: Kluwer) pp 457-60
10. Hermann G, Janus C, Schwartz I, Krivisky B, Bier S and Rabinowitz J 1987 Nonpalpable breast lesions: accuracy of prebiopsy mammographic diagnosis Radiology 165 323-6
11. Huo Z, Giger M, Vyborny C, Bick U, Lu P, Wolverton D and Schmidt R 1995 Analysis of spiculation in the computerized classification of mammographic masses Med. Phys. 22 1569-79
12. Huo Z, Giger M, Vyborny C, Wolverton D and Metz C 2000 Computerized classification of benign and malignant masses on digitized mammograms: a study of robustness Acad. Radiol. 7 1077-84
13. Huo Z, Giger M, Vyborny C, Wolverton D, Schmidt R and Doi K 1998 Automated computerized classification of malignant and benign masses on digitized mammograms Acad. Radiol. 5 155-68
14. Jain A K 1989 Fundamentals of Digital Imaging Processing International Editions (Englewood Cliffs, NJ: Prentice-Hall)
15. Karssemeijer N 1998 Automated classification of parenchymal patterns in mammograms Phys. Med. Biol. 43 365-78
16. Karssemeijer N and te Brake G 1996 Detection of stellate distortions in mammograms IEEE Trans. Med. Imaging 15 611-9
17. Koenderink J and van Doorn A 1992 Generic neighborhood operators IEEE Trans. Pattern Anal. Mach. Intell. 14 597-605
18. Lim W and Er M 2004 Classification of mammographic masses using generalized dynamic fuzzy neural networks Med. Phys. 31 1288-95
19. Lo S, Li H, Wang Y, Kinnard L and Freedman M 2002 A multiple circular path convolution neural network system for detection of mammographic masses IEEE Trans. Med. Imaging 21 150-8
20. Metz C, Herman B and Shen J 1998 Maximum-likelihood estimation of receiver operating characteristic curve (roc) curves from continuously- distributed data Statist. Med. 17 1033-53
21. Metz C, Wang P and Kronman H 1984 A new approach for testing the significance of differences between roc curves measured from correlated data Information Processing in Medical Imaging: Proc. 8th Conf. pp 432-45
22. Meyer J, Eberlein T, Stomper P and Sonnenfeld M 1990 Biopsy of occult breast lesions. Analysis of 1261 abnormalities JAMA 263 2341-3
23. Mudigonda N, Rangayyan R and Desautels J 2000 Gradient and texture analysis for the classification of mammographic masses IEEE Trans. Med. Imaging 19 1032-43
24. Nishikawa R, Giger M, Doi K, Metz C, Yin F, Vyborny C and Schmidt R 1994 Effect of case selection on the performance of computer-aided detection schemes Med. Phys. 21 265-9
25. Otten J, Karssemeijer N, Hendriks J, Groenewoud J, Francheboud J, Verbeek A, de Koning H and Holland R 2005 Effect of recall rate on earlier detection of breast cancers based on the Dutch performance indicators J. Natl Cancer Inst. 97 748-54
26. Rangayyan R, El-Faramawy N, Desautels J and Alim O 1997 Measures of acutance and shape for classification of breast tumors IEEE Trans. Med. Imaging 16 799-810
27. Sahiner B, Chan H, Petrick N, Helvie M and Goodsitt M 1998a Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis Med. Phys. 25 516-26
28. Sahiner B, Chan H, Petrick N, Helvie M and Goodsitt M 1998b Design of a high-sensitivity classifier based on a genetic algorithm: application to computer-aided diagnosis Phys. Med. Biol. 43 2853-71
29. Sahiner B, Chan H, Petrick N, Helvie M and Hadjiiski L 2001 Improvement of mammographic mass characterization using spiculation measures and morphological features Med. Phys. 28 1455-65
30. Tabar L, Vitak B, Chen H, Yen M, Duffy S and Smith R 2001 Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality Cancer 91 1724-31
31. te Brake G and Karssemeijer N 1999 Single and multiscale detection of masses in digital mammograms IEEE Trans. Med. Imaging 18 628-39
32. te Brake G, Karssemeijer N and Hendriks J 2000 An automatic method to discriminate malignant masses from normal tissue in digital mammograms Phys. Med. Biol. 45 2843-57
33. Timp S and Karssemeijer N 2004 A new 2D-segmentation method based on dynamic programming applied to computer aided detection in mammography Med. Phys. 31 958-71
34. van Vliet L, Young I and Verbeek P 1998 Recursive Gaussian derivative filters Proc. 14th Int. Conf. on Pattern Recognition vol I (Los Alamitos, CA: IEEE Computer Society Press) pp 509-14
35. Young I and van Vliet L 1995 Recursive implementation of the Gaussian filter Signal Process. 44 139-51
36. Zheng L and Chan A 2001 An artificial intelligent algorithm for tumor detection in screening mammogram IEEE Trans. Med. Imaging 20 559-67