On minimum variance thresholding
Pattern Recognition Letters
Thresholding based on variance and intensity contrast
Pattern Recognition
Expert Systems with Applications: An International Journal
Masseter segmentation using an improved watershed algorithm with unsupervised classification
Computers in Biology and Medicine
Supervised grayscale thresholding based on transition regions
Image and Vision Computing
Unsupervised range-constrained thresholding
Pattern Recognition Letters
A new social and momentum component adaptive PSO algorithm for image segmentation
Expert Systems with Applications: An International Journal
Characteristic analysis of Otsu threshold and its applications
Pattern Recognition Letters
An efficient method for segmentation of images based on fractional calculus and natural selection
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Image bilevel thresholding based on stable transition region set
Digital Signal Processing
MARGA: Multispectral Adaptive Region Growing Algorithm for brain extraction on axial MRI
Computer Methods and Programs in Biomedicine
A marker-based watershed method for X-ray image segmentation
Computer Methods and Programs in Biomedicine
Entropy maximization based segmentation, transmission and Wavelet Fusion of MRI images
International Journal of Hybrid Intelligent Systems
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A novel thresholding approach to confine the intensity frequency range of the object based on supervision is introduced. It consists of three steps. First, the region of interest (ROI) is determined in the image. Then, from the histogram of the ROI, the frequency range in which the proportion of the background to the ROI varies is estimated through supervision. Finally, the threshold is determined by minimizing the classification error within the constrained variable background range. The performance of the approach has been validated against 54 brain MR images, including images with severe intensity inhomogeneity and/or noise, CT chest images, and the Cameraman image. Compared with nonsupervised thresholding methods, the proposed approach is substantially more robust and more reliable. It is also computationally efficient and can be applied to a wide class of computer vision problems, such as character recognition, fingerprint identification, and segmentation of a wide variety of medical images.