High throughput technologies have increased the need for automated image analysis in a wide variety of microscopy techniques. Geometric active contour models provide a solution to automated image segmentation by incorporating statistical information in the detection of object boundaries. A statistical active contour may be defined by taking into account the optimisation of an information-theoretic measure between object and background. We focus on a product-type measure of divergence known as Cauchy-Schwartz distance which has numerical advantages over ratio-type measures. By using accurate shape derivation techniques, we define a new geometric active contour model for image segmentation combining Cauchy-Schwartz distance and Gabor energy texture filters. We demonstrate the versatility of this approach on images from the Brodatz dataset and phase-contrast microscopy images of cells.
CITATION STYLE
Biga, V., & Coca, D. (2017). Information-theoretic active contour model for microscopy image segmentation using texture. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10477 LNBI, pp. 12–26). Springer Verlag. https://doi.org/10.1007/978-3-319-67834-4_2
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