SU-D-201-06: Random Walk Algorithm Seed Localization Parameters in Lung Positron Emission Tomography (PET) Images

Authors

  • Soufi M,

    1. Shahid Beheshti University, Tehran, Tehran
    2. Shahid Beheshti University, Tehran, Iran., Tehran, Tehran
    3. Shariati Hospital, Tehran, Iran., Tehran, Tehran
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  • Asl A Kamali,

    1. Shahid Beheshti University, Tehran, Tehran
    2. Shahid Beheshti University, Tehran, Iran., Tehran, Tehran
    3. Shariati Hospital, Tehran, Iran., Tehran, Tehran
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  • Geramifar P

    1. Shahid Beheshti University, Tehran, Tehran
    2. Shahid Beheshti University, Tehran, Iran., Tehran, Tehran
    3. Shariati Hospital, Tehran, Iran., Tehran, Tehran
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Abstract

Purpose:

The objective of this study was to find the best seed localization parameters in random walk algorithm application to lung tumor delineation in Positron Emission Tomography (PET) images.

Methods:

PET images suffer from statistical noise and therefore tumor delineation in these images is a challenging task. Random walk algorithm, a graph based image segmentation technique, has reliable image noise robustness. Also its fast computation and fast editing characteristics make it powerful for clinical purposes. We implemented the random walk algorithm using MATLAB codes. The validation and verification of the algorithm have been done by 4D-NCAT phantom with spherical lung lesions in different diameters from 20 to 90 mm (with incremental steps of 10 mm) and different tumor to background ratios of 4:1 and 8:1. STIR (Software for Tomographic Image Reconstruction) has been applied to reconstruct the phantom PET images with different pixel sizes of 2×2×2 and 4×4×4 mm3. For seed localization, we selected pixels with different maximum Standardized Uptake Value (SUVmax) percentages, at least (70%, 80%, 90% and 100%) SUVmax for foreground seeds and up to (20% to 55%, 5% increment) SUVmax for background seeds. Also, for investigation of algorithm performance on clinical data, 19 patients with lung tumor were studied. The resulted contours from algorithm have been compared with nuclear medicine expert manual contouring as ground truth.

Results:

Phantom and clinical lesion segmentation have shown that the best segmentation results obtained by selecting the pixels with at least 70% SUVmax as foreground seeds and pixels up to 30% SUVmax as background seeds respectively. The mean Dice Similarity Coefficient of 94% ± 5% (83% ± 6%) and mean Hausdorff Distance of 1 (2) pixels have been obtained for phantom (clinical) study.

Conclusion:

The accurate results of random walk algorithm in PET image segmentation assure its application for radiation treatment planning and diagnosis.

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