Finding CLS Using Multiresolution Oriented Local Energy Feature Detection
In this paper, we present a novel technique for the detection of the curvilinear structures (CLS) in a mammogram based on a multiresolution, oriented local energy analysis. Local energy enables the detection not only of linear structures; but also features of several different kinds in a unified framework. It is possible to distinguish between such feature types using the local phase. In a separate post-processing stage, the behaviour of energy over multiple scales can be used to determine a) whether a response is due to a feature or to noise and b) to estimate at each location the local width of a CLS. Orientation information computed from steerable filters is used in the same post-processing stage to distinguish between curvilinear structures and speck-like responses such as microcalcifications which, on a micro-scale, resemble CLS. By combining scale, phase and orientation information we can distinguish the CLS from non-CLS locally linear features as well as localised structures with high gradients and thus remove only the CLS whilst leaving the remaining important image information intact.
KeywordsMammographic Density Local Energy Orientation Information Central Ridge Local Width
Unable to display preview. Download preview PDF.
- 1.N. Cerneaz and M. Brady. Finding curvilinear structures in mammograms. In Nicholas Ayache, editor, Computer Vision, Virtual Reality and Robotics in Medicine, Lecture Notes in Computer Science. Springer-Verlag, April 1995.Google Scholar
- 2.C.J. Evans, K. Yates, and Michael Brady. Statistical characterisation of normal curvilinear structures in mammograms. In Proc. Sixth Digital Image Computing Techniques and Applications DICTA, Melbourne, Australia, 2002.Google Scholar
- 4.P. Kovesi. Invariant measures of image features from phase information. PhD thesis, University of Western Australia, May 1996.Google Scholar
- 5.Veit U.B. Schenk. Visual Identification of Fine Surface Incisions. PhD thesis, University of Oxford, March 2002.Google Scholar
- 6.R. Zwiggelaar, T.C. Parr, and C.J. Taylor. Finding orientated line patterns in digital mammographie images. In Proc. 7th British Machine Vision Conf., Edinburgh, pages 715–724, Edinburgh, UK, September 1996.Google Scholar