Pattern Recognition
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Automatic thresholding of gray-level pictures using two-dimensional entropy
Computer Vision, Graphics, and Image Processing
Performance study of several global thresholding techniques for segmentation
Computer Vision, Graphics, and Image Processing
An analysis of histogram-based thresholding algorithms
CVGIP: Graphical Models and Image Processing
Maximum entropy segmentation based on the autocorrelation function of the image histogram
Journal of Computing and Information Technology
Graphical Models and Image Processing
Zoom-invariant vision of figural shape: the mathematics of cores
Computer Vision and Image Understanding
Maximum segmented image information thresholding
Graphical Models and Image Processing
User-steered image segmentation paradigms: live wire and live lane
Graphical Models and Image Processing
Operations Useful for Similarity-Invariant Pattern Recognition
Journal of the ACM (JACM)
Scale-based fuzzy connected image segmentation: theory, algorithms, and validation
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
A Mathematical Theory of Communication
A Mathematical Theory of Communication
A simple unsupervised MRF model based image segmentation approach
IEEE Transactions on Image Processing
Image thresholding by maximizing the index of nonfuzziness of the 2-D grayscale histogram
Computer Vision and Image Understanding
Robust Gray-Level Histogram Gaussian Characterisation
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Using connected components to guide image understanding and segmentation
Machine Graphics & Vision International Journal
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Image segmentation based on maximum-likelihood estimation and optimum entropy-distribution (MLE-OED)
Pattern Recognition Letters
Adaptive Smoothing via Contextual and Local Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-scale morphological modeling of a class of structural texture
Machine Graphics & Vision International Journal
On minimum variance thresholding
Pattern Recognition Letters
Thresholding based on variance and intensity contrast
Pattern Recognition
A novel image thresholding method based on Parzen window estimate
Pattern Recognition
Expert Systems with Applications: An International Journal
Supervised grayscale thresholding based on transition regions
Image and Vision Computing
Computer-Based Identification of Breast Cancer Using Digitized Mammograms
Journal of Medical Systems
Optimal multi-level thresholding using a two-stage Otsu optimization approach
Pattern Recognition Letters
An Improved Adaptive Smoothing Method
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Automatic seeded region growing for color image segmentation
Image and Vision Computing
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Unsupervised colour image segmentation using dual-tree complex wavelet transform
Computer Vision and Image Understanding
Image segmentation using adaptively selected color space
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Graph search with appearance and shape information for 3-D prostate and bladder segmentation
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
A new social and momentum component adaptive PSO algorithm for image segmentation
Expert Systems with Applications: An International Journal
Modified bacterial foraging algorithm based multilevel thresholding for image segmentation
Engineering Applications of Artificial Intelligence
Mathematical and Computer Modelling: An International Journal
An efficient method for segmentation of images based on fractional calculus and natural selection
Expert Systems with Applications: An International Journal
Local gaussian distribution fitting based FCM algorithm for brain MR image segmentation
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Scale-adaptive segmentation and recognition of individual trees based on LiDAR data
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Engineering Applications of Artificial Intelligence
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Computer Vision and Image Understanding
Entropy maximization based segmentation, transmission and Wavelet Fusion of MRI images
International Journal of Hybrid Intelligent Systems
Maximum similarity thresholding
Digital Signal Processing
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Thresholding is a popular image segmentation method that converts a gray-level image into a binary image. The selection of optimum thresholds has remained a challenge over decades. Besides being a segmentation tool on its own, often it is also a step in many advanced image segmentation techniques in spaces other than the image space. Most of the thresholding methods reported to date are based on histogram analysis using information-theoretic approaches. These methods have not harnessed the information captured in image morphology. Here, we introduce a novel thresholding method that accounts for both intensity-based class uncertainty驴a histogram-based property驴and region homogeneity驴an image morphology-based property. A scale-based formulation is used for region homogeneity computation. At any threshold, intensity-based class uncertainty is computed by fitting a Gaussian to the intensity distribution of each of the two regions segmented at that threshold. The theory of the optimum thresholding method is based on the postulate that objects manifest themselves with fuzzy boundaries in any digital image acquired by an imaging device. The main idea here is to select that threshold at which pixels with high class uncertainty accumulate mostly around object boundaries. To achieve this, a new threshold energy criterion is formulated using class-uncertainty and region homogeneity such that, at any image location, a high energy is created when both class uncertainty and region homogeneity are high or both are low. Finally, the method selects that threshold which corresponds to the minimum overall energy. The method has been compared to a recently-published maximum segmented image information ($MSII$) method. Superiority of the proposed method was observed both qualitatively on clinical medical images as well as quantitatively on 250 realistic phantom images generated by adding different degrees of blurring, noise, and background variation to real objects segmented from clinical images.