Image thresholding is a challenging task in image processing field. Many efforts have already been made to propose universal, robust methods to handle a wide range of images. This paper introduces a new optimization-based thresholding approach. The optimizer, Differential Evolution (DE) algorithm, minimizes dissimilarity between the input grey-level image and the bi-level (thresholded) image. T...

After highlighting vessel like structure by an appropriate filter in Matched Filter (MF) technique, thresholding strategy is needed for the automated detection of blood vessels in retinal images. For the purpose, we propose to use a new technique of entropic thresholding based on Gray Level Spatial Correlation (GLSC) histogram which takes into account the image local property. Results obtained ...

Thresholding process is a fundamental image processing method. Typical thresholding methods are based on partitioning pixels in an image into two clusters. A new thresholding method is presented, in this paper. The main contribution of the proposed approach is the detection of an optimal image threshold exploiting the empirical mode decomposition (EMD) algorithm. The EMD algorithm can decompose...

In this study we conduct an exhaustive survey of image thresholding methods, categorize them and express their formulae under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as: histogram shape, measurement space clustering, entropy, object attributes, spatial correlation and loca...

Image thresholding is one of the most powerful techniques for image segmentation, but it is not always satisfactory in applications under uneven illuminations. Adaptive image thresholding is used to find the optimal window for solving the illumination problem. In this paper, a novel window selection method for adaptive local thresholding is proposed. Based on simulated annealing, the proposed a...

In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. 3-16). We attempt to evaluate the...

Image denoising is getting more significance, especially in Computed Tomography (CT), which is an important and most common modality in medical imaging. This is mainly due to that the effectiveness of clinical diagnosis using CT image lies on the image quality. The denoising technique for CT images using window-based Multi-wavelet transformation and thresholding shows the effectiveness in denoi...