Mammography visual enhancement in CAD-based breast cancer diagnosis

Clinical Imaging

Volumn 37, Issue 2, 2013, Pages 273-282

  Wang, Hongting ^a   Li, Jun Bao ^a   Wu, Ligang ^a   Gao, Huijun ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Computer aided detection; Image matting; Tissue removed mammography

Indexed keywords

ADIPOSE TISSUE; BREAST CANCER DIAGNOSIS; COMPUTER-AIDED DETECTION; HEALTHY TISSUES; IMAGE MATTING; IMAGING CONTRAST; SINGLE IMAGES; VISUAL EFFECTS;

DISEASES; HISTOLOGY; TISSUE;

MAMMOGRAPHY;

ADIPOSE TISSUE; ARTICLE; BREAST CANCER; CANCER DIAGNOSIS; COMPUTER ASSISTED DIAGNOSIS; CONTRAST ENHANCEMENT; DIAGNOSTIC IMAGING; EARLY DIAGNOSIS; HISTOPATHOLOGY; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; MAMMARY GLAND; MAMMOGRAPHY; PRIORITY JOURNAL; VALIDITY;

ALGORITHMS; BREAST NEOPLASMS; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; MAMMOGRAPHY; RADIOGRAPHIC IMAGE ENHANCEMENT; SUBTRACTION TECHNIQUE;

EID: 84875257807     PISSN: 08997071     EISSN: 18734499     Source Type: Journal    
DOI: 10.1016/j.clinimag.2012.04.018     Document Type: Article

References (24)

1. American National Cancer Institute at the National Institutes of Health. Surveillance epidemiology and end results, http://seer.cancer.gov/statfacts/html/breast.html.
2. American cancer society. Breast cancer facts & figures 2011-2012, http://www.cancer.org/index.
3. Huo Z, Giger M, Vyborny C, Wolverton D, Schmidt R, Doi K Automated computerized classification of malignant and benign mass lesions on digital mammograms. Acad Radiol 1998, 5:155-168.
4. Setiono R Generating concise and accurate classification rules for breast cancer diagnosis. Artif Intell Med 2000, 18:205-219.
5. Tourassi GD, Markey MK, Lo JY, Floyd CE A neural network approach to breast cancer diagnosis as a constraint satisfaction problem. Med Phys 2001, 28:804-811.
6. Chen D, Chang RF, Huang YL Breast cancer diagnosis using self-organizing map for sonography. Ultrasound Med Biol 2000, 26:405-411.
7. Elmore JG, Armstrong K, Lehman CD, Fletcher SW Screening for breast cancer. JAMA 2005, 293(10):1245-1246.
8. Pelin G, Ahmet S Mammographic mass classification using wavelet based support vector machine. IU-J Electrical Electronics Eng 2009, 9(1):867-875.
9. Kaganami HG, Ali SK, Zou B Optimal approach for texture analysis and classification based on wavelet transform and neural network. J Inf Hiding Multimedia Signal Process 2011, 2(1):33-40.
10. Suganthi M, Madheswaran M An improved medical decision support system to identify the breast cancer using mammogram. J Med Syst 2012, 36(1):79-91.
11. Dua S, Singh H, Thompson HW Associative classification of mammograms using weighted rules. Expert Syst Appl 2009, 36(5):9250-9259.
12. Bozek J, Mustra M, Delac K, Grgic M A survey of image processing algorithms in digital mammography. Stud Comput Intell 2009, 231:631-657.
13. Cheng HD, Cai X, Chen X, Hu L, Lou X Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognit 2003, 36(12):2967-2991.
14. van Schie G, Karssemeijer N Detection of microcalcifications using a nonuniform noise model. Lect Notes Comput Sci 2008, 2008(5116):378-384.
15. Manglem Singh KH Fuzzy rule based median filter for gray-scale images. J Inf Hiding Multimedia Signal Process 2011, 2(2):108-122.
16. Mencattini A, Salmeri M, Lojacono R, Frigerio M, Caselli F Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing. Instrum Meas 2008, 57(7):1422-1430.
17. Veni S, Narayanankutty KA. Vision-based hexagonal image processing using Hex-Gabor. Signal, Image and Video Processing 2012; doi:10.1007/s11760-012-0293-5.
18. He K, Sun J, Tang X Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 2011, 33:2341-2353.
19. Apostoloff NE, Fitzgibbon AW Bayesian video matting using learnt image priors. Proc 2004 IEEE Comput Soc Conf CVPR 2004, 1:407-414.
20. Sun J, Jia J, Tang C-K, Shum H-Y Poisson matting. ACM SIGGRAPH Conf Proc 2004, 23(3):315-321.
21. Wang J, Cohen M An iterative optimization approach for unified image segmentation and matting. Proc IEEE Int Conf Comput Vis 2005, 2:936-943.
22. Hao Y, Atallah Mikhail J Running max/min filters using 1+o(1) comparisons per sample. IEEE Trans Pattern Anal Mach Intell 2011, 33:2544-2548.
23. Anat L, Dani L, Yair W A closed-form solution to natural image matting. IEEE Trans Pattern Anal Mach Intell 2008, 30:228-242.
24. Suckling J, Parker J, Dance DR, Astley S, et al. The Mammographic Image Analysis Society digital mammogram database. Excerpta Med 1994, 1069:375-378.