Abstract
In this paper, we propose a novel quantity to measure the complexity of regions with inhomogeneous intensity in images. In order to describe real boundaries of objects, we further design an edge detector which is based on the similarity between object regions and those around them. Imbedding these two measurements of inhomogeneous regions into a level set framework, the proposed model is applied to segment HCC regions in CT images with promising results. Additionally, benefitting from the two measurements, segmentation is robust with respect to the initialization. Comparison results also confirm that the proposed method is more accurate than two well-known methods, the CV model and the BCS model, on segmenting objects with inhomogeneities.
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Acknowledgements
This work is supported by National Nature Science Foundation of China (No. 91330101 and NO.11531005).
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Gui, L., Yang, X. (2019). Intensity Inhomogeneity Quantization-Based Variational Model for Segmentation of Hepatocellular Carcinoma (HCC) in Computed Tomography (CT) Images. In: Jiang, M., Ida, N., Louis, A., Quinto, E. (eds) The Proceedings of the International Conference on Sensing and Imaging. ICSI 2017. Lecture Notes in Electrical Engineering, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-319-91659-0_5
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