Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET

M Hatt, F Lamare, N Boussion, A Turzo, C Collet, F Salzenstein, C Roux, P Jarritt, K Carson, C Cheze-Le Rest, D Visvikis
Phys. Med. Biol.. 2007-05-18; 52(12): 3467-3491
DOI: 10.1088/0031-9155/52/12/010

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1. Phys Med Biol. 2007 Jun 21;52(12):3467-91. Epub 2007 May 18.

Fuzzy hidden Markov chains segmentation for volume determination and quantitation
in PET.

Hatt M(1), Lamare F, Boussion N, Turzo A, Collet C, Salzenstein F, Roux C,
Jarritt P, Carson K, Cheze-Le Rest C, Visvikis D.

Author information:
(1)INSERM U650, Laboratoire du Traitement de l’Information Médicale (LaTIM), CHU
Morvan, Bat 2bis (I3S), 5 avenue Foch, Brest, 29609, France.

Accurate volume of interest (VOI) estimation in PET is crucial in different
oncology applications such as response to therapy evaluation and radiotherapy
treatment planning. The objective of our study was to evaluate the performance of
the proposed algorithm for automatic lesion volume delineation; namely the fuzzy
hidden Markov chains (FHMC), with that of current state of the art in clinical
practice threshold based techniques. As the classical hidden Markov chain (HMC)
algorithm, FHMC takes into account noise, voxel intensity and spatial
correlation, in order to classify a voxel as background or functional VOI.
However the novelty of the fuzzy model consists of the inclusion of an estimation
of imprecision, which should subsequently lead to a better modelling of the
‘fuzzy’ nature of the object of interest boundaries in emission tomography data.
The performance of the algorithms has been assessed on both simulated and
acquired datasets of the IEC phantom, covering a large range of spherical lesion
sizes (from 10 to 37 mm), contrast ratios (4:1 and 8:1) and image noise levels.
Both lesion activity recovery and VOI determination tasks were assessed in
reconstructed images using two different voxel sizes (8 mm3 and 64 mm3). In order
to account for both the functional volume location and its size, the concept of %
classification errors was introduced in the evaluation of volume segmentation
using the simulated datasets. Results reveal that FHMC performs substantially
better than the threshold based methodology for functional volume determination
or activity concentration recovery considering a contrast ratio of 4:1 and lesion
sizes of

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