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10 March 2006 Automatic segmentation of vessels in breast MR sequences as a false positive elimination technique for automatic lesion detection and segmentation using the shape tensor
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Abstract
We present a new algorithm for automatic detection of bright tubular structures and its performance for automatic segmentation of vessels in breast MR sequences. This problem is interesting because vessels are the main type of false positive structures when automatically detecting lesions as regions that enhance after injection of the contrast agent. Our algorithm is based on the eigenvalues of what we call the shape tensor. It is new in that it does not rely on image derivatives of either first order, like methods based on the eigenvalues of the mean structure tensor, or second order, like methods based on the eigenvalues of the Hessian. It is therefore more precise and less sensitive to noise than those methods. In addition, the smoothing of the output which is inherent to approaches based on the Hessian or structure tensor is avoided. The output of our filter does not present the typical over-smoothed look of the output of the two differential filters that affects both their precision and sensitivity. The scale selection problem appears also less difficult in our approach compared to the differential techniques. Our algorithm is fast, needing only a few seconds per sequence. We present results of testing our method on a large number of motion-corrected breast MR sequences. These results show that our algorithm reliably segments vessels while leaving lesions intact. We also compare our method to the differential techniques and show that it significantly out-performs them both in sensitivity and localization precision and that it is less sensitive to scale selection parameters.
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Gerardo Hermosillo and Xuguang Jiang "Automatic segmentation of vessels in breast MR sequences as a false positive elimination technique for automatic lesion detection and segmentation using the shape tensor", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61440I (10 March 2006); https://doi.org/10.1117/12.654010
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