2016
DOI: 10.1109/tcsvt.2016.2539839
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A Simple Algorithm of Superpixel Segmentation with Boundary Constraint

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Cited by 45 publications
(42 citation statements)
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“…In the first module, 2D fluorescence images are loaded as a stack, and the user selects ROIs using a polygon tool; multiple ROIs can be selected for each loaded image. A segmentation process is implemented which divides each ROI into smaller segments using either a moving square algorithm or a simple linear iterative clustering algorithm (SLIC) (for more details see Supplementary Note 3) [44][45][46] . The pixel locations for each segment are saved and paired with the corresponding source image.…”
Section: Methodsmentioning
confidence: 99%
“…In the first module, 2D fluorescence images are loaded as a stack, and the user selects ROIs using a polygon tool; multiple ROIs can be selected for each loaded image. A segmentation process is implemented which divides each ROI into smaller segments using either a moving square algorithm or a simple linear iterative clustering algorithm (SLIC) (for more details see Supplementary Note 3) [44][45][46] . The pixel locations for each segment are saved and paired with the corresponding source image.…”
Section: Methodsmentioning
confidence: 99%
“…In Machairas et al (2015), the image gradient information is used to constrain the superpixel boundaries, but the results on superpixel evaluation metrics are lower than the ones of SLIC (Achanta et al, 2012). In Zhang et al (2016), the local gradient information is considered to improve the superpixel boundaries evolution. However, the computational cost of the method is increased by a 10× order of magnitude compared to SLIC.…”
Section: Contour Constraintmentioning
confidence: 99%
“…32, the circularity metric was introduced to locally evaluate the compactness of the superpixels. This measure is the usual local regularity metric, and has been considered in state-ofthe-art works, 11,16,30,40 and benchmarks. 34,37,41 This circularity C is defined for a superpixel shape S as follows:…”
Section: Regularitymentioning
confidence: 99%