Quality measures for fully automatic CT histogram-based fat estimation on a corpse sample

Author:

Schenkl Sebastian,Hubig Michael,Muggenthaler Holger,Shanmugam Jayant Subramaniam,Güttler Felix,Heinrich Andreas,Teichgräber Ulf,Mall Gita

Abstract

AbstractIn a previous article a new algorithm for fully automatic ‘CT histogram based Fat Estimation and quasi-Segmentation’ (CFES) was validated on synthetic data, on a special CT phantom, and tested on one corpse. Usage of said data in FE-modelling for temperature-based death time estimation is the investigation’s number one long-term goal. The article presents CFES’s results on a human corpse sample of size R = 32, evaluating three different performance measures: the τ-value, measuring the ability to differentiate fat from muscle, the anatomical fat-muscle misclassification rate D, and the weighted distance S between the empirical and the theoretical grey-scale value histogram. CFES-performance on the sample was: D = 3.6% for weight exponent α = 1, slightly higher for α ≥ 2 and much higher for α ≤ 0. Investigating τ, S and D on the sample revealed some unexpected results: While large values of τ imply small D-values, rising S implies falling D and there is a positive linear relationship between τ and S. The latter two findings seem to be counter-intuitive. Our Monte Carlo analysis detected a general umbrella type relation between τ and S, which seems to stem from a pivotal problem in fitting Normal mixture distributions.

Funder

Friedrich-Schiller-Universität Jena

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference30 articles.

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2. Schenkl, S. et al. Automatic CT-based finite element model generation for temperature-based death time estimation: Feasibility study and sensitivity analysis. Int. J. Legal Med. 131, 699–712. https://doi.org/10.1007/s00414-016-1523-0 (2017).

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