Abstract
In this paper, we proposed an algorithm for spiculated mass detection using digital down-sampled mammography images. In the algorithm, two-resulotion data is generated with a wavelet transform; For each resolution data, two gradient vector flow fields features, along with the standard deviation of a local edge orientation histogram, the mean, the standard deviation, and the standard deviation of the folded gradient orientations are extracted; a neural network classifier is used to generate spiculated mass masks; and the masks are filtered based on local relative intensity of the mammography images. The algorithm was tested using 200 mammograms including 100 massive images and 100 normal images from DDSM [17], in which FPI/TP of 1.0/0.88 and area of 0.71 under the ROC curve were obtained. The experimental results showed that the proposed method is efficient and robust.
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Zou, F., Zheng, Y., Zhou, Z., Agyepong, K. (2008). Gradient Vector Flow Fields and Spiculated Mass Detection in Digital Mammography Images. In: Krupinski, E.A. (eds) Digital Mammography. IWDM 2008. Lecture Notes in Computer Science, vol 5116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70538-3_42
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DOI: https://doi.org/10.1007/978-3-540-70538-3_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70537-6
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