Multiresolution-based watersheds for efficient image segmentation
Pattern Recognition Letters
Fast image segmentation based on multi-resolution analysis and wavelets
Pattern Recognition Letters
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Image thresholding using Tsallis entropy
Pattern Recognition Letters
A multistage adaptive thresholding method
Pattern Recognition Letters
Connectivity-based local adaptive thresholding for carotid artery segmentation using MRA images
Image and Vision Computing
Robust watershed segmentation using wavelets
Image and Vision Computing
Boundary refinements for wavelet-domain multiscale texture segmentation
Image and Vision Computing
Editorial: Medical image segmentation: Quo Vadis
Computer Methods and Programs in Biomedicine
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This paper presents a strategy for segmenting urinary sediment based on wavelet, morphology and combination method. Firstly, the wavelet transforms and morphology are used to get rid of the effect of the defocusing and get the subimages that include the particles. Then based on the characteristics of the subimages, edge detection and adaptive thresholding are employed adaptively. Finally, a simplified watershed algorithm for the overlapping particles is used. The experiment results show that the method can segment the defocusing urinary sediment images effectively and precisely.