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Proceedings Paper

Image segmentation using joint spatial-intensity-shape features: application to CT lung nodule segmentation
Author(s): Xujiong Ye; Musib Siddique; Abdel Douiri; Gareth Beddoe; Greg Slabaugh
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Paper Abstract

Automatic segmentation of medical images is a challenging problem due to the complexity and variability of human anatomy, poor contrast of the object being segmented, and noise resulting from the image acquisition process. This paper presents a novel feature-guided method for the segmentation of 3D medical lesions. The proposed algorithm combines 1) a volumetric shape feature (shape index) based on high-order partial derivatives; 2) mean shift clustering in a joint spatial-intensity-shape (JSIS) feature space; and 3) a modified expectation-maximization (MEM) algorithm on the mean shift mode map to merge the neighboring regions (modes). In such a scenario, the volumetric shape feature is integrated into the process of the segmentation algorithm. The joint spatial-intensity-shape features provide rich information for the segmentation of the anatomic structures or lesions (tumors). The proposed method has been evaluated on a clinical dataset of thoracic CT scans that contains 68 nodules. A volume overlap ratio between each segmented nodule and the ground truth annotation is calculated. Using the proposed method, the mean overlap ratio over all the nodules is 0.80. On visual inspection and using a quantitative evaluation, the experimental results demonstrate the potential of the proposed method. It can properly segment a variety of nodules including juxta-vascular and juxta-pleural nodules, which are challenging for conventional methods due to the high similarity of intensities between the nodules and their adjacent tissues. This approach could also be applied to lesion segmentation in other anatomies, such as polyps in the colon.

Paper Details

Date Published: 27 March 2009
PDF: 9 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72594V (27 March 2009); doi: 10.1117/12.811151
Show Author Affiliations
Xujiong Ye, Medicsight PLC (United Kingdom)
Musib Siddique, Medicsight PLC (United Kingdom)
Abdel Douiri, Medicsight PLC (United Kingdom)
Gareth Beddoe, Medicsight PLC (United Kingdom)
Greg Slabaugh, Medicsight PLC (United Kingdom)


Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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