The Otsu s method has been proven as an efficient method in image segmentation for bi-level thresholding. However, this method is computationally intensive when extended to multi-level thresholding. In this paper, we present a hybrid optimization scheme for multiple thresholding by the criteria of (1) Otsu s minimum within-group variance and (2) Gaussian function fitting. Four example images ar...

The paper addresses the problem of image segmentation in 2D domain by means of maximizing rough entropy measure in granular computing setting. Proposed 2D multilevel rough entropy thresholding extends multilevel thresholding scheme into 2D image thresholding. Proposed thresholding algorithm 2D MRET has been compared with standard multilevel thresholding based on Otsu method. Experimental result...

automatic road extraction from aerial images can be very helpful in traffic control and vehicle guidance systems. most of the road detection approaches are based on image segmentation algorithms. color-based segmentation is very sensitive to light changes and consequently the change of weather condition affects the recognition rate of road detection systems. in order to reduce the light change ...

Variance-based thresholding methods could be biased from the threshold found by expert and the underlying mechanism responsible for this bias is explored in this paper. An analysis on the minimum class variance thresholding (MCVT) and the Otsu method, which minimizes the within-class variance, is carried out. It turns out that the bias for the Otsu method is due to differences in class variance...

The multilevel thresholding problem is a challenge task due to the fact that the computation is usually very time-consuming for obtaining the optimal multilevel thresholds. Though the state-of-the-art multilevel thresholding algorithms applied various meta-heuristic techniques or acceleration strategies, they still directly searched the optimal thresholds in the whole histogram only for the fix...

Variance-based thresholding method is a very effective technology for image segmentation. However, its performance is limited in traditional one-dimensional and twodimensional scheme. In this paper, a novel two-dimensional variance thresholding scheme to improve image segmentation performance is proposed. The two-dimensional histogram of the original and local average image is projected to one-...

The main objective of medical image segmentation is to extract and characterize anatomical structures with respect to some input features or expert knowledge. The Otsu method is a popular non-parametric method in medical image segmentation. Traditional Otsu method for medical image segmentation is time–consuming computation and became an obstacle in real time application systems. In the same wa...

A key aspect in extracting quantitative information from FMI logs is to segment the FMI image to get image of layers. In this paper, an automatic method based on FCM clustering and Otsu thresholding is introduced in order to extract quantitative information from FMI images. All pixels are clustered using FCM clustering algorithm at the f irst step. The second step uses KNN for other clustering....

Image segmentation is one of the important tasks in computer vision and image processing. Thresholding is a simple but most effective technique in segmentation. It based on classify image pixels into object and background depended on the relation between the gray level value of the pixels and the threshold. Otsu technique is a robust and fast thresholding techniques for most real world images w...

Renal biopsy image segmentation is an important step in computer-aided diagnosis. Among all available segmentation techniques, automatic thresholding methods are widely employed because of their advantages of simple implementation and time-saving. The Otsu method is one of these thresholding methods and is frequently applied in various fields. The two-dimensional (2-D) Otsu method is superior t...