Integrating shape into an interactive segmentation framework

S. Kamalakannan; B. Bryant; H. Sari-Sarraf; R. Long; S. Antani; G. Thoma

doi:10.1117/12.2007262

28 February 2013 Integrating shape into an interactive segmentation framework

S. Kamalakannan, B. Bryant, H. Sari-Sarraf, R. Long, S. Antani, G. Thoma

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 867030 (2013) https://doi.org/10.1117/12.2007262
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

This paper presents a novel interactive annotation toolbox which extends a well-known user-steered segmentation framework, namely Intelligent Scissors (IS). IS, posed as a shortest path problem, is essentially driven by lower level image based features. All the higher level knowledge about the problem domain is obtained from the user through mouse clicks. The proposed work integrates one higher level feature, namely shape up to a rigid transform, into the IS framework, thus reducing the burden on the user and the subjectivity involved in the annotation procedure, especially during instances of occlusions, broken edges, noise and spurious boundaries. The above mentioned scenarios are commonplace in medical image annotation applications and, hence, such a tool will be of immense help to the medical community. As a first step, an offline training procedure is performed in which a mean shape and the corresponding shape variance is computed by registering training shapes up to a rigid transform in a level-set framework. The user starts the interactive segmentation procedure by providing a training segment, which is a part of the target boundary. A partial shape matching scheme based on a scale-invariant curvature signature is employed in order to extract shape correspondences and subsequently predict the shape of the unsegmented target boundary. A ‘zone of confidence’ is generated for the predicted boundary to accommodate shape variations. The method is evaluated on segmentation of digital chest x-ray images for lung annotation which is a crucial step in developing algorithms for screening Tuberculosis.

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S. Kamalakannan, B. Bryant, H. Sari-Sarraf, R. Long, S. Antani, and G. Thoma "Integrating shape into an interactive segmentation framework", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867030 (28 February 2013); https://doi.org/10.1117/12.2007262

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KEYWORDS

Image segmentation

Lung

Chest imaging

Algorithm development

Detection and tracking algorithms

Stars

Digital imaging

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