Abstract
A method of extracting salient image features in mammograms at multiple scales using the monogenic signal is presented. The derived local phase provides structure information (such as edge, ridge etc.) while the local amplitude encodes the local brightness and contrast information. Together with the simultaneously computed orientation, these three pieces of information can be used for mammogram segmentation including locating the inner breast edge which is important for quantitative breast density assessment. Due to the contrast invariant property of the local phase, the algorithm proves to be very reliable on an extensive datasets of images obtained from various sources and digitized by different scanners.
This is a preview of subscription content, log in via an institution to check access.
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
Mallat, S., Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, New York (1998)
Morrone, M.C., Owens, R.A.: Feature detection from local energy. Pattern Recognition Letters 6, 303–313 (1987)
Kovesi, P.: Invariant Measures of Image Features from Phase Information. Videre: Journal of Computing Vision Research 1 (1999)
Felsberg, M.: Low-Level Image Processing with the Structure Multivector. In: Institut fur Informatik und Praktische Mathematik. KIEL: Christian-Albrechts-Universitat (2002)
Bick, U., Giger, M.L., Schmidt, R.A., Nishikawa, R.M., Wolverton, D.E., Doi, K.: Automated segmentation of digitized mammograms. Acad. Radiol. 2, 1–9 (1995)
Mendez, A.J., Tahoces, P.G., Lado, M.J., Souto, M., Correa, J.L., Vidal, J.J.: Automatic detection of breast border and nipple in digital mammograms. Comput. Methods Programs Biomed. 49, 253–262 (1996)
Karssemeijer, N., Brake, G.: Combining single view features and asymmetry for detection of mass lesions. Presented at International Workshop on Digital Mammography (1998)
Petroudi, S., Brady, M.: A Method for Automatic Breast Border Segmentation. Presented at Medical Image Analysis and Understanding, Sheffield, UK (2003)
Chandrasekhar, R., Attikiouzel, Y.: Automatic breast border segmentation by background modeling and subtraction. Presented at International Workshop on Digital Mammography (2000)
Goodsitt, M.M., Chan, H.P., Liu, B., Guru, S.V., Morton, A.R., Keshavmurthy, S., Petrick, N.: Classification of compressed breast shapes for the design of equalization filters in x-ray mammography. Med. Phys. 25, 937–948 (1998)
Ojala, T., Nappi, J., Nevalainen, O.: Accurate segmentation of the breast region from digitized mammograms. Comput. Med. Imaging Graph. 25, 47–59 (2001)
Ferrari, R.J., Rangayyan, R.M., Desautels, J.E., Borges, R.A., Frere, A.F.: Identification of the breast boundary in mammograms using active contour models. Med. Biol. Eng. Comput. 42, 201–208 (2004)
Highnam, R., Pan, X., Brady, J.M., Ancelin, B., Dixon, K., Warren, R., Jeffreys, M.: SMF As a Reprocucible Estimation of Breast Density. Presented at IWDM (2004)
Jeffreys, M., Warren, R., Highnam, R., Davey, S.G.: Initial experience of using an automated volumetric measure of breast density: the standard mammogram form (SMF). British Journal of Radiology (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pan, XB., Brady, M., Highnam, R., Declerck, J. (2006). The Use of Multi-scale Monogenic Signal on Structure Orientation Identification and Segmentation. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds) Digital Mammography. IWDM 2006. Lecture Notes in Computer Science, vol 4046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11783237_81
Download citation
DOI: https://doi.org/10.1007/11783237_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35625-7
Online ISBN: 978-3-540-35627-1
eBook Packages: Computer ScienceComputer Science (R0)