PURPOSE: The Tofts and the extended Tofts models are the pharmacokinetic models commonly used in dynamic contrast-enhanced MRI (DCE-MRI) perfusion analysis, although they do not provide two important biological markers, namely, the plasma flow and the permeability-surface area product. Estimates of such markers are possible using advanced pharmacokinetic models describing the vascular distribution phase, such as the tissue homogeneity model. However, the disadvantage of the advanced models lies in biased and uncertain estimates, especially when the estimates are computed voxelwise. The goal of this work is to improve the reliability of the estimates by including information from neighboring voxels. THEORY AND METHODS: Information from the neighboring voxels is incorporated in the estimation process through spatial regularization in the form of total variation. The spatial regularization is applied on five maps of perfusion parameters estimated using the tissue homogeneity model. Since the total variation is not differentiable, two proximal techniques of convex optimization are used to solve the problem numerically. RESULTS: The proposed algorithm helps to reduce noise in the estimated perfusion-parameter maps together with improving accuracy of the estimates. These conclusions are proved using a numerical phantom. In addition, experiments on real data show improved spatial consistency and readability of perfusion maps without considerable lowering of the quality of fit. CONCLUSION: The reliability of the DCE-MRI perfusion analysis using the tissue homogeneity model can be improved by employing spatial regularization. The proposed utilization of modern optimization techniques implies only slightly higher computational costs compared to the standard approach without spatial regularization.

• Klíčová slova
• DCE-MRI, perfusion parameter estimation, proximal methods, spatial regularization, tissue homogeneity model, total variation,
• MeSH
• algoritmy MeSH
• fantomy radiodiagnostické MeSH
• glioblastom diagnostické zobrazování   MeSH
• kontrastní látky farmakologie   MeSH
• krysa rodu Rattus MeSH
• magnetická rezonanční tomografie * MeSH
• mozek diagnostické zobrazování   MeSH
• nádory mozku diagnostické zobrazování   MeSH
• perfuze MeSH
• permeabilita MeSH
• počítačová simulace MeSH
• počítačové zpracování obrazu MeSH
• poměr signál - šum MeSH
• reprodukovatelnost výsledků MeSH
• zvířata MeSH
• Check Tag
• krysa rodu Rattus MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• kontrastní látky MeSH

The so-called copy-move forgery, based on copying an object and pasting in another location of the same image, is a common way to manipulate image content. In this paper, we address the problem of copy-move forgery detection in JPEG images. The main problem with JPEG compression is that the same pixels, after moving to a different position and storing in the JPEG format, have different values. The majority of existing algorithms is based on matching pairs of similar patches, which generates many false matches. In many cases they cannot be eliminated by postprocessing, causing the failure of detection. To overcome this problem, we derive a JPEG-based constraint that any pair of patches must satisfy to be considered a valid candidate and propose an efficient algorithm to verify the constraint. The constraint can be integrated into most existing methods. Experiments show significant improvement of detection, especially for difficult cases, such as small objects, objects covered by textureless areas and repeated patterns.

• Klíčová slova
• Copy-move modification, Forgery, Image tampering, Quantization constraint set,
• Publikační typ
• časopisecké články MeSH

This paper introduces a set of methods for image and video forensic analysis. They were designed to help to assess image and video credibility and origin and to restore and increase image quality by diminishing unwanted blur, noise, and other possible artifacts. The motivation came from the best practices used in the criminal investigation utilizing images and/or videos. The determination of the image source, the verification of the image content, and image restoration were identified as the most important issues of which automation can facilitate criminalists work. Novel theoretical results complemented with existing approaches (LCD re-capture detection and denoising) were implemented in the PIZZARO software tool, which consists of the image processing functionality as well as of reporting and archiving functions to ensure the repeatability of image analysis procedures and thus fulfills formal aspects of the image/video analysis work. Comparison of new proposed methods with the state of the art approaches is shown. Real use cases are presented, which illustrate the functionality of the developed methods and demonstrate their applicability in different situations. The use cases as well as the method design were solved in tight cooperation of scientists from the Institute of Criminalistics, National Drug Headquarters of the Criminal Police and Investigation Service of the Police of the Czech Republic, and image processing experts from the Czech Academy of Sciences.

• Klíčová slova
• Image forensic analysis, Image restoration, Image source identification, Image tampering detection,
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Retinal images are essential clinical resources for the diagnosis of retinopathy and many other ocular diseases. Because of improper acquisition conditions or inherent optical aberrations in the eye, the images are often degraded with blur. In many common cases, the blur varies across the field of view. Most image deblurring algorithms assume a space-invariant blur, which fails in the presence of space-variant (SV) blur. In this work, we propose an innovative strategy for the restoration of retinal images in which we consider the blur to be both unknown and SV. We model the blur by a linear operation interpreted as a convolution with a point-spread function (PSF) that changes with the position in the image. To achieve an artifact-free restoration, we propose a framework for a robust estimation of the SV PSF based on an eye-domain knowledge strategy. The restoration method was tested on artificially and naturally degraded retinal images. The results show an important enhancement, significant enough to leverage the images' clinical use.

• MeSH
• algoritmy MeSH
• angiografie metody   MeSH
• artefakty MeSH
• astigmatismus diagnóza   MeSH
• diagnostické techniky oftalmologické * MeSH
• fundus oculi MeSH
• lidé MeSH
• normální rozdělení MeSH
• optika a fotonika MeSH
• počítačové zpracování obrazu MeSH
• reprodukovatelnost výsledků MeSH
• retina patologie   MeSH
• retinální cévy patologie   MeSH
• rozpoznávání automatizované metody   MeSH
• statistické modely MeSH
• zrak MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

We examine the problem of restoration from multiple images degraded by camera motion blur. We consider scenes with significant depth variations resulting in space-variant blur. The proposed algorithm can be applied if the camera moves along an arbitrary curve parallel to the image plane, without any rotations. The knowledge of camera trajectory and camera parameters is not necessary. At the input, the user selects a region where depth variations are negligible. The algorithm belongs to the group of variational methods that estimate simultaneously a sharp image and a depth map, based on the minimization of a cost functional. To initialize the minimization, it uses an auxiliary window-based depth estimation algorithm. Feasibility of the algorithm is demonstrated by three experiments with real images.

• MeSH
• algoritmy * MeSH
• artefakty * MeSH
• interpretace obrazu počítačem metody   MeSH
• počítačové zpracování signálu * MeSH
• pohyb těles MeSH
• reprodukovatelnost výsledků MeSH
• rozpoznávání automatizované metody   MeSH
• senzitivita a specificita MeSH
• umělá inteligence * MeSH
• uživatelské rozhraní počítače MeSH
• vylepšení obrazu metody   MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH