Abstract
Purpose
Breast masses exhibit variability in margins, shapes, and dimensions, so their detection is a difficult task in mammographic computer-aided diagnosis. Mass detection is usually a two-step procedure: mass identification and false-positive reduction. A new method to automatically detect mass lesions in mammographic images with tuning according to the breast tissue density was developed and tested.
Methods
A modified phase portrait analysis method was introduced, based on the eigenvalue condition number and an eigenvalue intensity map. The method uses an iterative and tissue density-adaptive segmentation procedure with extraction of geometric features. False-positive reduction is accomplished using a fuzzy inference-based classifier. A leave-one-image-out cross-validation procedure was implemented, and stepwise regression analysis was used to automatically extract an optimal set of features. Testing and validation were performed on two different data sets containing at least one malignant mass D1 (388 images) and D2 (674 images), and a third data set N1 (50 images) was used consisting of normal controls. These three data sets were taken from the Digital Database for Screening Mammography.
Results
For sensitivities of 0.9, 0.85, 0.80, and 0.75, the best results on cancer images exhibit an False-Positive per Image (FPpI) equal to 0.6, 0.45, 0.35, and 0.3, respectively, using a Bayes Linear Discriminant Analysis (LDA) classifier and an FPpI of 0.85, 0.7, 0.55, and 0.45 using a fuzzy inference system (FIS) for false-positive reduction. When the algorithm is tested on normal images, an FPpI equal to 0.4, 0.3, 0.25, and 0.2 was observed using LDA and 0.3, 0.25, 0.2, and 0.15 using the FIS.
Conclusion
A preclinical study of an automatic breast mass detection algorithm provided promising results in terms of sensitivity and low false-positive rate. Further development and clinical testing are justified based on the results.
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References
Mazzarella F, Bazzocchi M (2007) Cad systems for mammography: a real opportunity? a review of the literature. La radiologia Medica 112(3): 329–353
Salmeri M, Mencattini A, Rabottino G, Accattatis A, Lojacono R (2009) Assisted breast cancer diagnosis environment: A tool for dicom mammographic images analysis. In: IEEE international workshop on medical measurements and applications (MEMEA ’09). Cetraro, Cosenza, Italy
Balleyguier C, Ayadi S, Van Nguyen K, Vanel D, Dromain C, Sigal R (2007) Birads classification in mammography. Eur J Radiol 61: 192–194
Mencattini A, Rabottino G, Salmeri M, Lojacono R (2010) A study on the automatic detection of clustered microcalcifications in mammograms. In: International symposium on applied sciences in biomedical and communication technologies (ISABEL ’10). Rome, Italy
Mencattini A, Rabottino G, Salmeri M, Lojacono R, Tamilia E (2011) Features extraction and fuzzy logic based classification for false positives reduction in mammographic images. In: International conference on bio-inspired systems and signal processing (BIOSIGNALS ’11). Rome, Italy
Mudigonda NR, Rangayyan RM (2001) Detection of breast masses in mammograms by density slicing and texture flow-field analysis. IEEE Trans Med Imaging 20(12): 1215–1227
Tang J, Rangayyan RM, Xu J, El Naqa I, Yang Y (2009) Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances. IEEE Trans Inf Tech Biomed 13(2): 236–251
Sampat MP, Markey MK, Bovik AC (2005) Computer-aided detection and diagnosis in mammography. Handbook of image and video processing, 2nd edn. Academic Press, New York, pp, pp 1195–1217
Timp S, Karssemeijer N (2004) A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography. Med Phys 31(5): 958–971
Kegelmeyer WP et al (1994) Computer-aided mammographic screening for speculated lesions. Radiology 191(2): 331–337
Liu S, Babbs CF, Delp EJ (2001) Multiresolution detection of spiculated lesions in digital mammograms. IEEE Trans Image Process 10(6): 874–884
Sampat MP, Bovik AC (2003) Detection of spiculated lesions in mammograms. Proc 25th Annu Int Conf IEEE Eng Med Biol Soc 1: 801–813
Campanini R et al (2004) A novel featureless approach to mass detection in digital mammograms based on support vector machines. Phys Med Biol 49(6): 961–975
Kom G, Tiedeu A, Kom M (2007) Automated detection of masses in mammograms by local adaptive thresholding. Comput Biol Med 37(1): 37–48
Eltonsy NH, Tourassi GD, Elmaghraby AS (2007) A concentric morphology model for the detection of masses in mammography. IEEE Trans Med Imaging 26(6): 880–889
Chan HP et al (1995) Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space. Phys Med Biol 40(5): 857–876
Sahiner B et al (1996) Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images. IEEE Trans Med Imaging 15(5): 598–610
Mudigonda NR, Rangayyan RM (2000) Desautels JEL Gradient and texture analysis for the classification of mammographic masses. IEEE Trans Med Imaging 19(10): 1032–1043
Wei J et al (2005) Computer aided detection of breast masses on full field digital mammograms. Med Phys 32(9): 2827–2837
Bellotti R et al (2006) A completely automated cad system for mass detection in a large mammographic database. Med Phys 33(8): 3066–3075
Heath M, Bowyer KW, Kopans D, Moore R, Kegelmeyer P (1998) Digital Mammography. Current status of the digital database for screening mammography. Kluwer, Dordrecht, pp 457–460
Mencattini A, Salmeri M (2011) Breast masses detection using phase portrait analysis, intrinsic coherence. In: Computer assisted radiology and surgey (CARS ’11). Berlin, Germany
Ferrari RJ, Rangayyan R, Desautels JEL, Borges R, Frere A (2004) Automatic identification of the pectoral muscle in mammograms. IEEE Trans Med Imaging 23(2): 232–245
Sun Y, Suri J, Rangayyan R, Desautels JEL (2006) New approach for breast skin-line estimation in mammograms. Recent advances in breast imaging, mammography, and computer-aided diagnosis of breast cancer. SPIE press, Bellingham
Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2): 266–277
Ayres FJ, Rangayyan RM (2007) Reduction of false positives in the detection of architectural distortion in mammograms by using a geometrically constrained phase portrait model. Int J CARS 1: 361–369
Wei J et al (2009) Computer-aided detection of breast masses on mammograms: Dual system approach with two-view analysis. Med Phys 36(10): 4451–4460
Andersson ER (2007) Fuzzy and rough techniques in medical diagnosis and medication. Studies in fuzziness and soft computing. Springer, Berlin
Ferrero A, Salicone S, Mencattini A, Rabottino G, Salmeri M (2010) Uncertainty evaluation in a fuzzy classifier for microcalcifications in digital mammography. In: IEEE instrumentation and measurement technology conference (IMTC ’10). Austin, TX, USA
Mencattini A et al (2011) Automatic breast masses boundary extraction in digital mammography using spatial fuzzy c-means clustering and active contour models. In: IEEE international workshop on medical measurements and applications (MEMEA ’11), Bari, Italy
Mencattini A et al (2011) A study on a novel scoring system for the evaluation of expected mortality in icu-patients. In: IEEE international workshop on medical measurements and applications (MEMEA ’11), Bari, Italy
JCGM (2008) Evaluation of measurement data—-an introduction to the guide to the expression of uncertainty in measurement 104:2009 and supplement 1 to the guide to the expression of uncertainty in measurement—propagation of distributions using a monte carlo method. Technical Report 100:2008, JCGM
Wirth MA (2006) Recent advances in breast imaging, mammography, and computer aided diagnosis of breast cancer. Performance evaluation of CADe algorithms in mammography. SPIE Press, Bellingham, pp pp 639–699
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Mencattini, A., Salmeri, M. Breast masses detection using phase portrait analysis and fuzzy inference systems. Int J CARS 7, 573–583 (2012). https://doi.org/10.1007/s11548-011-0659-0
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DOI: https://doi.org/10.1007/s11548-011-0659-0