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Proceedings Paper

Development of a semi-automated combined PET and CT lung lesion segmentation framework
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Paper Abstract

Segmentation is one of the most important steps in automated medical diagnosis applications, which affects the accuracy of the overall system. In this paper, we propose a semi-automated segmentation method for extracting lung lesions from thoracic PET/CT images by combining low level processing and active contour techniques. The lesions are first segmented in PET images which are first converted to standardised uptake values (SUVs). The segmented PET images then serve as an initial contour for subsequent active contour segmentation of corresponding CT images. To evaluate its accuracy, the Jaccard Index (JI) was used as a measure of the accuracy of the segmented lesion compared to alternative segmentations from the QIN lung CT segmentation challenge, which is possible by registering the whole body PET/CT images to the corresponding thoracic CT images. The results show that our proposed technique has acceptable accuracy in lung lesion segmentation with JI values of around 0.8, especially when considering the variability of the alternative segmentations.

Paper Details

Date Published: 13 March 2017
PDF: 6 pages
Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101370B (13 March 2017); doi: 10.1117/12.2256808
Show Author Affiliations
Farli Rossi, Univ. Kebangsaan Malaysia (Malaysia)
Siti Salasiah Mokri, Univ. Kebangsaan Malaysia (Malaysia)
Ashrani Aizzuddin Abd. Rahni, Univ. Kebangsaan Malaysia (Malaysia)


Published in SPIE Proceedings Vol. 10137:
Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor Gimi, Editor(s)

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