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Automatic segmentation of lungs in SPECT images using active shape model trained by meshes delineated in CT images
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- Grigorios-Aris, Cheimariotis (author)
- Aristotle University of Thessaloniki
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- Al-Mashat, Mariam (author)
- Lund University,Lunds universitet,Hjärt-MR-gruppen i Lund,Forskargrupper vid Lunds universitet,Lund Cardiac MR Group,Lund University Research Groups,Skåne University Hospital
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- Kostas, Haris (author)
- Aristotle University of Thessaloniki
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- 2016
- 2016
- English 4 s.
- In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. - 9781457702204 ; 2016-October, s. 1280-1283
- Conference paper (peer-reviewed)
Abstract
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- This paper presents a fully automated method for segmentation of 3D SPECT ventilation and perfusion images. It relies on statistical information on lung shape derived by CT manual segmentation and its main processing steps are: shape model extraction, binary segmentation, positioning of mean shape in SPECT images and iterative shape adaptation based on intensity profiles and on what is considered 'plausible' lung shape. The Active Shape Model is used to generate accurate anatomic results in SPECT images with functional information and thus unclear borders, especially in the case of pathologies. The method was compared against ground truth manual segmentation on CT images, using volumetric, difference dice coefficient, sensitivity and precision.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
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