In this work, a robust thresholding algorithm framework based on reconstruction and dimensionality reduction of the three-dimensional (3-D) histogram is proposed with the consideration of the poor anti-noise performan...
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In this work, a robust thresholding algorithm framework based on reconstruction and dimensionality reduction of the three-dimensional (3-D) histogram is proposed with the consideration of the poor anti-noise performance in existing 3-D histogram-based segmentation methods due to the obviously wrong region division. Firstly, our method reconstructs the 3-D histogram based on the distribution of noisy points which reduce its segmentation performance. Secondly, we transfer the region division in 3D histogram from eight partitions into two parts, thus reducing the searching space of threshold from 3-dimension to 1-dimension, which saves a lot of processing time and memory space. Thirdly, we apply the presented framework to global thresholding algorithms such as Otsu method, minimumerror method, and maximum entropy method and so on, and propose corresponding robust global thresholding algorithms. Finally, segmentation result and running time are given at the end of this paper compared with those of 3-D Otsu's method, Otsu method, minimumerror method and maximum entropy method. The experimental results show that the presented method has better anti-noise performance and visual quality compared with the above four approaches, and has lower time complexity than 3-D Otsu's method.
Image segmentation is the pre-step of multi-target tracing in Computer Assisted Sperm Motion Analysis System (CASMA). As a special sperm-tracing problem, a fast, automatic, unsupervised segmentation algorithm is requi...
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ISBN:
(纸本)0819446564
Image segmentation is the pre-step of multi-target tracing in Computer Assisted Sperm Motion Analysis System (CASMA). As a special sperm-tracing problem, a fast, automatic, unsupervised segmentation algorithm is required. In this paper, we utilize four segmentation algorithms to segment three different kinds of sperm images sampled from our actual system. By making an overall comparison between them, a conclusion is reached that the Otsu's maximum between-class variance algorithm is the most suitable for the special sperm microscopic image segmentation and this segmentation algorithm has been successfully applied to our developed system.
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