Abstract
Clinically, segmentation has many benefits for effective patient management, both in terms of pre-operative planning and post-operative assessment. Volumetric image segmentation of medical data still remains as a major challenge, largely due to the complexities of in-vivo anatomical structures, cross-subject and cross-modality variations. This correspondence presents a semiautomatic segmentation algorithm that is based on graph and chaos theory. Also, we introduce a new weighting function in the method for accurate delineation of regions of interest in medical images that contain regional inhomogeneities; the preliminary results show the potential of the proposed technique.
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Dakua, S.P., Abi-Nahed, J., Al-Ansari, A. (2014). Self Stabilization of Image Attributes for Left Ventricle Segmentation. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2013. Lecture Notes in Computer Science, vol 8330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54268-8_29
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DOI: https://doi.org/10.1007/978-3-642-54268-8_29
Publisher Name: Springer, Berlin, Heidelberg
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