Malignant and benign clustered microcalcifications: Automated feature analysis and classification

Radiology

Volumn 198, Issue 3, 1996, Pages 671-678

  Jiang, Yulei ^a   Nishikawa, Robert M ^a   Wolverton, Dulcy E ^a   Metz, Charles E ^a   Giger, Maryellen L ^a   Schmidt, Robert A ^a   Vyborny, Carl J ^a,b   Doi, Kunio ^a  

^a UNIVERSITY OF CHICAGO   (United States)

^b La Grange Memorial Hospital ^*  (United States)

Author keywords

Breast neoplasms, calcification; Breast neoplasms, diagnosis; Computers, diagnostic aid; Computers, neural network

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; BENIGN TUMOR; BREAST BIOPSY; BREAST CALCIFICATION; BREAST CANCER; COMPUTER ANALYSIS; DIAGNOSTIC ACCURACY; DIFFERENTIAL DIAGNOSIS; HUMAN; MAJOR CLINICAL STUDY; MALIGNANT NEOPLASTIC DISEASE; MAMMOGRAPHY; PRIORITY JOURNAL;

EID: 0030062375     PISSN: 00338419     EISSN: None     Source Type: Journal    
DOI: 10.1148/radiology.198.3.8628853     Document Type: Article

References (26)

1. Sickles EA. Mammographic features of 300 consecutive nonpalpable breast cancers. AJR 1986; 146:661-663.
2. Hall FM, Storella JM, Silverstone DZ, Wyshak G. Nonpalpable breast lesions: recommendations for biopsy based on suspicion of carcinoma at mammography. Radiology 1988; 167:353-358.
3. Kopans DB. The positive predictive value of mammography. AJR 1992; 158:521-526.
4. Knutzen AM, Gisvold JJ. Likelihood of malignant disease for various categories of mammographically detected, nonpalpable breast lesions. Mayo Clin Proc 1993; 68: 454-460.
5. Getty DJ, Pickett RM, D'Orsi CJ, Swets JA. Enhanced interpretation of diagnostic images. Invest Radiol 1988; 23:240-252.
6. Cook HM, Fox MD. Application of expert systems to mammographic image analysis. Am J Physiol Imaging 1989; 4:16-22.
7. Wu Y, Giger ML, Doi K, Vyborny CJ, Schmidt RA, Metz CE. Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. Radiology 1993; 187:81-87.
8. Magnin IE, Alaoui ME, Brémond A. Automatic microcalcifications pattern recognition from x-ray mammographies. Proc SPIE 1989; 1137:170-175.
9. Dhawan AP, Chitre Y, Moskowitz M. Artificial neural network based classification of mammographic microcalcifications using image structure features. Proc SPIE 1993; 1905:820-831.
10. Parker J, Dance DR, Davies DH. Classification of ductal carcinoma in-situ by image analysis of calcifications from mammograms. Proc SPIE 1993; 1905:832-840.
11. Shen L, Rangayyan RM, Leo Desautels JE. Application of shape analysis to mammographic calcifications. IEEE Trans Med Imaging 1994; 13:263-274.
12. Moskowitz M. The predictive value of certain mammographic signs in screening for breast cancer. Cancer 1983; 51:1007-1011.
13. Sickles EA. Breast calcifications: mammographic evaluation. Radiology 1986; 160: 289-293.
14. Doi K, Giger ML, Nishikawa RM, et al. Digital radiography: a useful clinical tool for computer-aided diagnosis by quantitative analysis of radiographic images. Acta Radiol 1993; 34:426-139.
15. Nishikawa RM, Jiang Y, Giger ML, et al. Performance of automated CAD schemes for the detection and classification of clustered microcalcifications. In: Gale AG, Astley SM, Dance DR, Cairns AY, eds. Digital mammography. Amsterdam, Holland: Elsevier, 1994; 13-20.
16. Jiang Y, Nishikawa RM, Giger ML, Doi K, Schmidt RA, Vyborny CJ. Method of extracting signal area and signal thickness of microcalcifications from digital mammograms. Proc SPIE 1992; 1778:28-36.
17. Tabár L, Dean PB. Teaching atlas of mammography. New York, NY: Thieme-Stratton, 1985.
18. Lanyi M. Diagnosis and differential diagnosis of breast calcifications. Berlin, Germany: Springer-Verlag, 1988.
19. Metz CE. ROC methodology in radiologic imaging. Invest Radiol 1986; 21:720-733.
20. Metz CE, Shen JH, Herman BA. New methods for estimating a binormal ROC curve from continuously-distributed test results. Presented at the 1990 Joint Statistical Meetings of the American Statistical Society and the Biometric Society, Anaheim, Calif, August 1990.
21. Metz CE. Some practical issues of experimental design and data analysis in radiological ROC studies. Invest Radiol 1989; 24:234-245.
22. McClish DK. Analyzing a portion of the ROC curve. Med Decis Making 1989; 9:190-195.
23. Baker JA, Kornguth PJ, Lo JY, Williford ME, Floyd CE. Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon. Radiology 1995; 196: 817-822.
24. Giger ML, Doi K, MacMahon H, et al. An intelligent workstation for computer-aided diagnosis. RadioGraphics 1993; 13:647-656.
25. Nishikawa RM, Haldemann RC, Papaioannou J, et al. Initial experience with a prototype clinical "intelligent" mammography workstation for computer-aided diagnosis. Proc SPIE 1995; 2434:65-71.
26. Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL, The PDF Research Group, eds. Parallel distributed processing: explorations in the microstructure of cognition. Vol 1, Foundations. Cambridge, Mass: MIT Press, 1986; 318-362.