Efficient Automated Detection and Segmentation of Medium and Large Liver Tumors: CAD Approach
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/1421
In this paper, we present a fully automated system that detects
and segments potential liver cancer tumors from a thin slice CT
data. The system is targeted toward a tumor whose volume is
larger than $1 cm^3$, and is efficient as the average
computation time per volume in our experiment is roughly 3.5
minutes.

The system first reduces the volume size by 4x4x4 to reduce the
computation and memory requirements. It then detects candidate
locations as local minima of the intensity fields after a
variant of textit{elastic quadratic} smoothing. It then
provides a rough segmentation at each candidate by fitting a
plane at sampled points near the periphery of the concave
region in the intensity profiles. The rough segmentation is
used to estimate the range of intensity values in the tumor,
which is used to obtain a more accurate segmentation by a
method originally developed for pulmonary nodules. The result
of the second segmentation is interpolated at the resolution of
the original data.

The development of the system is a part of the 2008 3D
Segmentation in the Clinic: A Grand Challenge competition. Four
CT volumes containing 10 tumors were used for the development
of the algorithm. Additional six CT volumes containing 10
tumors were used to test the segmentation performance.

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plus my review by Xiang Deng on 07-25-2008 for revision #1
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Keywords: computer-aided detection, classfication, smoothing, diffusion
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