Abstract
The widespread use of CT imaging and the critical importance of early detection of epidural masses of the spinal canal generate a scenario ideal for the implementation of a computer-aided detection (CAD) system. Epidural masses can lead to paralysis, incontinence and loss of neurological function if not promptly detected. We present, to our knowledge, the first CAD system to detect epidural masses on CT. In this paper, global intensity and local spatial features are modeled as spatially constrained Gaussian Mixture Model (CGMM) for epidural mass detection. The Cross-validation on 23 patients with epidural masses on body CT showed that the CGMM yielded a marked improvement of performance (69 % at 8.6 false positives per patient) over an intensity based K-means method (46 % at 7.9 false-positives per patient).
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Pattanaik, S. et al. (2014). Epidural Masses Detection on Computed Tomography Using Spatially-Constrained Gaussian Mixture Models. In: Yao, J., Klinder, T., Li, S. (eds) Computational Methods and Clinical Applications for Spine Imaging. Lecture Notes in Computational Vision and Biomechanics, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-07269-2_9
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DOI: https://doi.org/10.1007/978-3-319-07269-2_9
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