Gradient Vector Flow Fields and Spiculated Mass Detection in Digital Mammography Images

Authors:
Fengmei Zou;Yufeng Zheng;Zhengdong Zhou;Kwabena Agyepong
Affiliations:
Dept. of Advanced Technologies, Alcorn State University, Lorman, USA MS 39096 and Dept. of Cognitive Science, Xiamen University, Xiamen, China 361005;Dept. of Advanced Technologies, Alcorn State University, Lorman, USA MS 39096;Dept. of Advanced Technologies, Alcorn State University, Lorman, USA MS 39096 and Dept. of Medical Physics, Nanjing University of Aeronautics and Astronautics, Nanjing, China 210096;Dept. of Advanced Technologies, Alcorn State University, Lorman, USA MS 39096
Venue:
IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
Year:
2008

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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.