Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Image Processing: The Fundamentals
Image Processing: The Fundamentals
Digital Image Processing
Evaluation for uncertain image classification and segmentation
Pattern Recognition
Thresholding based on variance and intensity contrast
Pattern Recognition
Image thresholding using type II fuzzy sets
Pattern Recognition
Image thresholding based on semivariance
Pattern Recognition Letters
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Segmentation is an important research area in image analysis. In particular, effective segmentation methods play an essential role in the computerization of the analysis, classification, and quantification of biological images for high content screening. Image segmentation based on thresholding has many practical and useful applications because it is simple and computationally efficient. Different methods based on different criteria of optimality give different choices of thresholds. This paper introduces a method for optimal thresholding in gray-scale images by mimizing the variograms of object and background pixels. The mathematical formulation of the proposed technique is very easy for computer implementation. The experimental results have shown the superior performance of the new method over some popular models for the segmentation cell images.