Real time mass classification for mammographic images: a Driven CADx scheme
DOI:
https://doi.org/10.34119/bjhrv6n3-429Keywords:
CADx scheme, digital mammography, digital mammogram processing, mass classification in mammographyAbstract
Computer-Aided Diagnosis (CADx) schemes have been proposed to serve as a supplementary image analysis tool in mammography. Experienced radiologists tend to be more assertive to such schemes in assisting their interpretation rather than solely relying on their ability to detect suspicious signals. This study focuses on a simplified version of a previously developed mammography CADx scheme, which was initially designed for digitized film, but is now specifically aimed at classifying breast nodules marked as regions of interest on digital images. This “driven” CADx scheme provides prompt indications regarding whether the selected nodule is deemed normal or suspicious. Its performance was evaluated through tests conducted on different mammograms sets – one with large number of images selected from DDSM database for training, testing and validation of classification parameters, and other comprising direct digital images from InBreast database. Remarkably, similar rates were observed for sensitivity, specificity and accuracy across these two sets (83%, 67% and 72%, respectively). The classification attributes were associated to contour, density and texture. Furthermore, a third test was conducted involving radiologists analyzing digital mammograms obtained from a specific full field digital mammography (FFDM) unit. Results showed that the Driven CADx scheme positively influenced the final diagnoses made by 3 radiologists, consistently increasing accuracy rates. This promising result allows establishing this software as a valuable tool for radiologists in the analysis of masses in digital mammography. The scheme can be implemented on any operating system, or even accessed online.
References
Kolb, T.; Lichy, J.; Newhouse, J. Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology, 2002, v. 225, p. 165-75.
Abreu, M. S. de A., et al. Breast microcalcifications in screening mammography: a literature review. Brazilian Journal of Health Review, 4(6), 2021, p. 27283–27289. (https://doi.org/10.34119/bjhrv4n6-292).
Georgian-Smith, D; Moore, R.H.; Halpern, E. Blinded Comparison of Computer-Aided Detection with Human Second Reading in Screening Mammography, AJR Women's Imaging, 2007, v. 189, p. 1135-1141.
Meyer, J.E.; Eberlein, T.J.; Stomper, P.C.; Sonnenfeld, M.R. Biopsy of occult breast lesions. Analysis of 1261 abnormalities, Journal of the American Medical Association, 1990, v. 263, p. 2341-2343.
Karssemeijer, N. CAD: more than a perception aid. Medical Physics Web, Winter 2011. p. 6.
Schiabel, H. et al. A prototype of mammography CADx scheme integrated to imaging quality evaluation techniques. In Proc. of SPIE Medical Imaging: Computer-aided Diagnosis, Orlando, FL, USA, v. 7963, p. 796323-1 - 796323-12, 12-17 Feb 2011.
ImageJ. Available online: https://imagej.nih.gov/ij/index.html (accessed on 12 Dec 2022).
Goes, R.F.; Schiabel, H.; Sousa, M.A.Z. Automatic scanning software based on the characteristic curve of mammograms digitizers. Journal of Electronic Imaging, 2013, v. 22, p. 1 – 9.
Matheus, B.R.N.; Marcomini, K.D.; Schiabel, H. Segmentation techniques evaluation based on a single compact breast mass classification scheme. In Proc. of SPIE Medical Imaging: Image Processing, San Diego, CA, USA, v. 9787, p. 978426-1 – 978426-9, 27 Feb – 02 Mar 2016.
Matheus, B.R.N.; Schiabel, H. Online mammographic images database for development and comparison of CAD schemes. Journal of Digital Imaging, 2011, v. 24, p. 500–506.
Heath, M.; Bowyer, K.; Kopans, D.; Moore, R.; Kegelmeyer, W.P. The digital database for screening mammography; In Proc. of the Fifth International Workshop on Digital Mammography, Medical Physics Publishing. 2001. p. 212-218.
Matheus, B.R.N.; Gonçalves, S.; Schiabel, H. Automated mass classification in CADx mammography scheme using density parameters – In Proc of 17th International Workshop on Computer-Aided Diagnosis (29th International Congress on Computer Assisted Radiology and Surgery – CARS2015), IJCARS, v. 10, suppl. 1, Barcelona, Spain, 24-27 Jun 2015, , p. S286.
Siegel, E.L. (Un. Maryland Med Ctr, USA). Re-thinking CAD for the future: a clinical perspective. Personal communication, SPIE Medical Imaging: Computer-aided Diagnosis, San Diego, CA, USA, 19 Feb 2014.
Haygood, T.M. et al. Timed efficiency of interpretation of digital and film-screen screening mammograms. American Journal of Roentgenology, 2009, v. 1, p. 216-220.
Kooi, T.; Mordang, J-J.; Karssemeijer, N. Conditional random field modelling of interactions between findings in mammography. In Proc. of SPIE Medical Imaging: Computer-aided Diagnosis, Orlando, FL, USA, v. 10134, p. 101341E-1 - 101341E-8, 11-16 Feb 2017.
Holland, K. et al. Quantification of mammographic masking risk with volumetric breast density maps: how to select women for supplemental screening. In Proc. of SPIE Medical Imaging: Computer-aided Diagnosis, San Diego, CA, USA, v. 9785, p. 97850I, 27 Feb – 03 Mar 2016.
S. Osher, J. A. Sethian, “Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations”. J. of Computational Physics, vol. 79, pp. 12-49, 1988.
Ribeiro, P.B.; Romero, R.A.; Oliveira, P.R.; Schiabel, H.; Verçosa, L.B. Automatic segmentation of breast masses using enhanced ICA mixture model. Neurocomputing, 2013, v. 120, p. 61-71.
Haralick, R.; Shanmugam, K.; Distein, I. Textural features for image classification, IEEE Trans. on Systems, Man, and Cybernetics - SMC, 1973, v. 3, p. 610-621.
Moreira, I. et al. INbreast: toward a full-field digital mammographic database, Academic radiology, 2012, v. 19, p. 236-48.
