Approaches for the analysis of statistical parametric maps (SPMs) can be crudely grouped into three main categories in which different philosophies are applied to delineate activated regions. These being type I error control thresholding, false discovery rate (FDR) control thresholding and posterior probability thresholding. To better understand the properties of these main approaches, we carri...

The method of wavelet thresholding for removing noise, or denoising, has been researched extensively due to its effectiveness and simplicity. Much of the literature has focused on developing the best uniform threshold or best basis selection. However, not much has been done to make the threshold values adaptive to the spatially changing statistics of images. Such adaptivity can improve the wave...

A single thresholding technique may not provide the best binarization for all images of datasets such as protein crystallization images. To overcome this limitation, multiple thresholding methods are used to binarize images. Whenever multiple thresholding techniques are used, it is important to know which one provides the best result automatically. To solve this problem, in this study, we propo...

In recent years, wavelet thresholding procedures have been widely applied to the field of nonparametric function estimation. They excel in the areas of spatial adaptivity, computational efficiency and asymptotic optimality. Among the various thresholding techniques studied in the literature, there are the term-byterm thresholding (hard, soft, . . . ) initially developed by Donoho and Johnstone ...

Image thresholding is a very common image processing operation, since all image processing schemes need some sort of operation of the pixels into different classes. In order to determine thresholds, most methods analyze the histogram of the image. The optimal thresholds are often found by either minimizing or maximizing an objective function with respect to the values of the thresholds. In this...

An adaptive local thresholding method for microscope based high content analysis (HCA) is proposed. Cell micrographs of HCA contain detailed objects both in and out of focus, which cannot be correctly segmented by global thresholding methods. The proposed method utilizes adaptive local neighborhood size and double thresholding, and is able to produce segmentations that conform closely to percep...

Image thresholding is one of the processing techniques to provide high quality preprocessed image. Image vagueness and bad illumination are common obstacles yielding in a poor image thresholding output. By assuming image as fuzzy sets, several different fuzzy thresholding techniques have been proposed to remove these obstacles during threshold selection. In this paper, we proposed an algorithm ...

-Maximum likelihood thresholding methods are presented on the basis of population mixture models. It turns out that the standard thresholding proposed by Otsu, which is based on a discriminant criterion and also minimizes the mean square errors between the original image and the resultant binary image, is equivalent to the maximization of the likelihood of the conditional distribution in the po...

Two algorithms for document image thresholding are presented, that are suitable for scanning document images at high-speed. They are designed to operate on a portion of the image while scanning the document, thus, they fit a pipeline architecture and lend themselves to real-time implementation. The first algorithm is based on adaptive thresholding and uses local edge information to switch betwe...

In this paper, we propose a new speech enhancement system using the wavelet thresholding algorithm. The basic wavelet thresholding algorithm has some defects including the assumption of white Gaussian noise (WGN), malfunction in unvoiced segments, bad auditory quality, etc. In the proposed system, we introduce a new algorithmwhich does not require any voiced/ unvoiced detection system.Also, in ...