The purpose of this study was to analyze the behavior of a contouring algorithm for PET images based on adaptive thresholding depending on lesions size and target-to-background (TB) ratio under different conditions of image reconstruction parameters. Based on this analysis, the image reconstruction scheme able to maximize the goodness of fit of the thresholding algorithm has been selected. A ph...

Brain activation studies based on PET or fMRI seek to explore neuroscience questions by statistically analyzing the acquired images to produce statistical parametric images (SPIs). An increasingly wide range of univariate and multivariate analysis techniques are used to generate SPIs in order to detect mean-signal activations and/or long-range spatial interactions. However, little is known abou...

Introduction:  Breast  cancer  is  a  leading  cause  of  death  among  females  throughout  the  world.  Currently,  radiologists are able to detect only 75% of breast cancer cases. Making use of computer-aided design (CAD)  can play an important role in helping radiologists perform more accurate diagnoses.   Material and Methods: Using our hybrid method, the background and the pectoral muscle...

Automatic Number Plate Recognition (ANPR) is an image-processing technology and an important field of research that identifies vehicles by their number plates in which the number plate information is extracted from vehicle's image or from sequence of images without direct human intervention. ANPR consists of 4 phases: Preprocessing, number plate extraction, character segmentation, character rec...

The construction of adaptive nonparametric procedures by means of wavelet thresholding techniques is now a classical topic in modern mathematical statistics. In this paper, we extend this framework to the analysis of nonparametric regression on sections of spin fiber bundles defined on the sphere. This can be viewed as a regression problem where the function to be estimated takes as its values ...

This paper presents an approach for a real-time region-based motion segmentation and tracking using an adaptive thresholding and k-means clustering in a scene, with focus on a video monitoring system. In order to reduce the computational load to the motion segmentation, the presented approach is based on the variation regions application of a weighted k-means clustering algorithm, followed by a...

In this paper, the segmentation using codebook index statistics (SUCIS) method is proposed for vector-quantized images. Three different codebooks are constructed according to the statistical characteristics (mean, variance, and gradient) of the codewords. Then they are employed to generate three different index images, which can be used to analyze the image contents including the homogeneous, e...

This paper studies the problem of Simultaneous Sparse Approximation (SSA). This problem arises in many applications which work with multiple signals maintaining some degree of dependency such as radar and sensor networks. In this paper, we introduce a new method towards joint recovery of several independent sparse signals with the same support. We provide an analytical discussion on the converg...

Most modern day image based object registration systems typically rely on one or two algorithms to perform registration across the entire spectrum of operating conditions. While such systems may perform well in one constrained environment, such as an indoor laboratory, they fail when used in varying conditions, such as outdoors. This paper proposes an adaptive system for image based object regi...