Breast cancer detection in gadolinium-enhanced mr images by static region descriptors and neural networks

Journal of Magnetic Resonance Imaging

Volumn 17, Issue 3, 2003, Pages 337-342

  Tzacheva, Angelina A ^a   Najarian, Kayvan ^a   Brockway, John P ^a,b,c,d  

^a University of North Carolina at Charlotte   (United States)

^b Memory Testing Corporation   (United States)

^c PRESBYTERIAN HEALTHCARE   (United States)

^d JPB   (United States)

Author keywords

Breast cancer; Magnetic resonance imaging; Neural networks; Region oriented segmentation

Indexed keywords

GADOLINIUM PENTETATE;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BREAST CANCER; CALCULATION; CANCER DIAGNOSIS; CLINICAL ARTICLE; CONTRAST ENHANCEMENT; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC IMAGING; DIAGNOSTIC VALUE; HEALTH CARE COST; HUMAN; IMAGE PROCESSING; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; SIGNAL PROCESSING; TUMOR VOLUME; VALIDATION PROCESS;

BREAST NEOPLASMS; CONTRAST MEDIA; FEMALE; GADOLINIUM; HUMANS; IMAGE ENHANCEMENT; MAGNETIC RESONANCE IMAGING; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER);

EID: 0037370061     PISSN: 10531807     EISSN: None     Source Type: Journal    
DOI: 10.1002/jmri.10259     Document Type: Article

References (13)

1. Ries L, Eisner M, Kosary C, Hankey B, Miller B, Clegg L, Edwards B, editors. Surveillance, epidemiology and end results (SEER) cancer statistics review. 1973-1997. Bethesda: National Cancer Institute; 2000.
2. Strickland RN, Hahn HI. Wavelet transforms for detecting microcalcifications in mammograms. IEEE Trans Med Imaging 1996;15:218-229.
3. James D, Clymer B, Schmalbrock P. Texture detection of simulated microcalcification susceptibility effects in magnetic resonance imaging of breasts. J Magn Reson Imaging 2001;13:886-881.
4. Frouge C, Guinebretiere JM, Contesso G, Di Paola R, Blery M. Correlation between contrast enhancement in dynamic magnetic resonance imaging of the breast and tumor angiogenesis. Invest Radiol 1994;1043-1049.
5. Haralick RM, Shanmugam K, Dinsein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1971;3:660-671.
6. Kelcz F, Santyr GE, Cron GO, Mongin SJ. Application of a quantitative model to differentiate benign from malignant breast lesions detected by dynamic, gadolinium-enhanced MRI. J Magn Reson Imaging 1996;6:743-752.
7. Lucht REA, Knopp MV, Brix G. Classification of signal-time curves from dynamic MR mammography by neural networks. Magn Reson Imaging 2001;19:51-57.
8. Gilles R, Meunier M, Lucidarme O, et al. Clustered breast microcalcifications: evaluation by dynamic contrast-enhanced subtraction MRI. J Comput Assist Tomogr 1996;20:9-14.
9. Cecil KM, Schanall MD, Siegelman ES, Lenkinski RE. The evaluation of human breast lesions with magnetic resonance imaging and proton magnetic resonance spectroscopy. Breast Cancer Res Treat 2001;68:45-54.
10. Schnall MD, Rosten S, Englander S, Orel SG, Nunes LW. A combined architectural and kinetic interpretation model for breast MR images. Acad Radiol 2001;8:7.
11. Schnall MD, Ikeda D. Lesion Diagnosis Working Group report. J Magn Reson Imaging 1999;10:982-990.
12. Gonzalez RC, Woods RE. Digital image processing. Boston: Addison-Wesley; 1993, 716 p.
13. Canny J. A computational approach to edge detection. IEEE Trans Patt Anal Mach Intel 1986;679-698.