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- W2589192915 abstract "Rice (Oryza sativa) breeders usually need to quantify the progress of their work by examining certain anatomical traits in the roots. These traits can be observed via a microscope. However, an efficient, accurate algorithm for image segmentation is required for quantitative analysis in a large dataset. This work proposes an automatic method based on graph optimization and multi-resolution analysis to segment and compute the area of the rice-root cortex from its cross-section image. This method extracts a rough region of the cortex in a coarse scale and locates likely boundary pixels in a fine scale. Each of these pixels is treated as a node in a graph. The method, then, selects some of these pixels to form an optimal cycle representing a cortex boundary. Optimality is mainly quantified by edge strength and curvature of a cycle and can be obtained by Dijkstra's shortest path algorithm. This formulation leads to a highly accurate and efficient algorithm (Dice similarity coefficient > 0.99 against manual segmentation and < 11 seconds of computation time per image on average)." @default.
- W2589192915 created "2017-03-03" @default.
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- W2589192915 date "2016-12-01" @default.
- W2589192915 modified "2023-09-24" @default.
- W2589192915 title "Rice-root cortex segmentation using graph optimization and multi-resolution analysis" @default.
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- W2589192915 doi "https://doi.org/10.1109/icsec.2016.7859889" @default.
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