Abstract
Segmentation is typically the first step in computer-aided-detection (CADe). The second step is false positive reduction which usually involves computing a large number of features with thresholds set by training over excessive data set. The number of false positives can, in principle, be reduced by extensive noise removal and other forms of image enhancement prior to segmentation. However, this can drastically affect the true positive results and their boundaries. We present a post-segmentation method to reduce the number of false positives by using a diffusion scale space. The method is illustrated using Integral Invariant scale space, though this is not a requirement. It is quite general, does not require any prior information, is fast and easy to compute, and gives very encouraging results. Experiments are performed both on intensity mammograms as well as on Volpara® density maps.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Nishikawa, R.M., Kallergi, M.: 6.3. Computer-aided detection, in its present form, is not an effective aid for screening mammography (2008) Colin, G.-T., Hend, W.R.:
Astley, S.M., Gilbert, F.J.: Computer-aided detection in mammography. Clin. Radiol. 59, 390–399 (2004)
Lladó, X., Oliver, A., Freixenet, J., Martí, R., Martí, J.: A textural approach for mass false positive reduction in mammography. Comput. Med. Imaging Graph. 33, 415–422 (2009)
Mudigonda, N.R., Rangayyan, R.M., Leo Desautels, J.E.: Detection of breast masses in mammograms by density slicing and texture flow-field analysis. IEEE Trans. Med. Imaging 20, 1215–1227 (2001)
Rangayyan, R.M.: Biomedical image analysis. CRC press (2004)
Li, L., Zheng, Y., Zhang, L., Clark, R.A.: False-positive reduction in CAD mass detection using a competitive classification strategy. Med. Phys. 28, 250–258 (2001)
Truong, Q.D., Nguyen, M.P., Hoang, V.T., Nguyen, H.T., Nguyen, D.T., Nguyen, T.D., Nguyen, V.D.: Feature Extraction and Support Vector Machine Based Classification for False Positive Reduction in Mammographic Images. In: Li, S., Jin, Q., Jiang, X., Park, J.J.(J.H.) (eds.) Frontier and Future Development of Information Technology in Medicine and Education. LNEE, pp. 921–929. Springer, Heidelberg (2014)
Manay, S., Hong, B.-W., Yezzi, A.J., Soatto, S.: Integral invariant signatures. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 87–99. Springer, Heidelberg (2004)
Janan, F., Brady, S.M.: Integral invariants for image enhancement. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4018–4021 (2013)
Janan, F., Brady, M., Tromans, C., Highnam, R.: Standard Attenuation Rate and Volpara(R) Volumetric Density Maps. In: Second MICCAI International Workshop on Breast Image Analysis, BIA 2013, Nagoya, Japan (2013)
Janan, F., Brady, M.: Shape matching by integral invariants on eccentricity transformed images. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5099–5102 (2013)
Janan, F., Brady, S.M.: Region matching in the temporal study of mammograms using integral invariant scale-space. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds.) IWDM 2012. LNCS, vol. 7361, pp. 173–180. Springer, Heidelberg (2012)
Hong, B.-W., Brady, M.: Segmentation of mammograms in topographic approach (2003)
Highnam, R., Brady, S.M., Yaffe, M.J., Karssemeijer, N., Harvey, J.: Robust breast composition measurement-volparaTM. In: Martí, J., Oliver, A., Freixenet, J., Martí, R. (eds.) IWDM 2010. LNCS, vol. 6136, pp. 342–349. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Janan, F., Brady, S.M., Highnam, R. (2014). False Positive Reduction in CADe Using Diffusing Scale Space. In: Fujita, H., Hara, T., Muramatsu, C. (eds) Breast Imaging. IWDM 2014. Lecture Notes in Computer Science, vol 8539. Springer, Cham. https://doi.org/10.1007/978-3-319-07887-8_83
Download citation
DOI: https://doi.org/10.1007/978-3-319-07887-8_83
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07886-1
Online ISBN: 978-3-319-07887-8
eBook Packages: Computer ScienceComputer Science (R0)