The analysis of medical images has been woven into the fabric of the pattern analysis and machine intelligence (PAMI) community since the earliest days of these Transactions. Initially, the efforts in this area were seen as applying pattern analysis and computer vision techniques to another interesting dataset. However, over the last two to three decades, the unique nature of the problems presented within this area of study have led to the development of a new discipline in its own right. Examples of these include: the types of image information that are acquired, the fully three-dimensional image data, the nonrigid nature of object motion and deformation, and the statistical variation of both the underlying normal and abnormal ground truth. In this paper, we look at progress in the field over the last 20 years and suggest some of the challenges that remain for the years to come.

https://hal.inria.fr/inria-00615100/document

Medical image analysis: progress over two decades and the challenges ahead

Medical image registration is an important task in medical image processing. It refers to the process of aligning data sets, possibly from different modalities (e.g., magnetic resonance and computed tomography), different time points (e.g., follow-up scans), and/or different subjects (in case of population studies). A large number of methods for image registration are described in the literature. Unfortunately, there is not one method that works for all applications. We have therefore developed elastix, a publicly available computer program for intensity-based medical image registration. The software consists of a collection of algorithms that are commonly used to solve medical image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. The command-line interface enables automated processing of large numbers of data sets, by means of scripting. The usage of elastix for comparing different registration methods is illustrated with three example experiments, in which individual components of the registration method are varied.

elastix : A Toolbox for Intensity-Based Medical Image Registration

Preface Through many centuries physics has been one of the most fruitful sources of inspiration for mathematics. As a consequence, mathematics has become an economic language providing a few basic principles which allow to explain a large variety of physical phenomena. Many of them are described in terms of partial diierential equations (PDEs). In recent years, however, mathematics also has been stimulated by other novel elds such as image processing. Goals like image segmentation, multiscale image representation, or image restoration cause a lot of challenging mathematical questions. Nevertheless, these problems frequently have been tackled with a pool of heuristical recipes. Since the treatment of digital images requires very much computing power, these methods had to be fairly simple. With the tremendous advances in computer technology in the last decade, it has become possible to apply more sophisticated techniques such as PDE-based methods which have been inspired by physical processes. Among these techniques, parabolic PDEs have found a lot of attention for smoothing and restoration purposes, see e.g. 113]. To restore images these equations frequently arise from gradient descent methods applied to variational problems. Image smoothing by parabolic PDEs is closely related to the scale-space concept where one embeds the original image into a family of subsequently simpler , more global representations of it. This idea plays a fundamental role for extracting semantically important information. The pioneering work of Alvarez, Guichard, Lions and Morel 11] has demonstrated that all scale-spaces fulllling a few fairly natural axioms are governed by parabolic PDEs with the original image as initial condition. Within this framework, two classes can be justiied in a rigorous way as scale-spaces: the linear diiusion equation with constant dif-fusivity and nonlinear so-called morphological PDEs. All these methods satisfy a monotony axiom as smoothing requirement which states that, if one image is brighter than another, then this order is preserved during the entire scale-space evolution. An interesting class of parabolic equations which pursue both scale-space and restoration intentions is given by nonlinear diiusion lters. Methods of this type have been proposed for the rst time by Perona and Malik in 1987 190]. In v vi PREFACE order to smooth the image and to simultaneously enhance semantically important features such as edges, they apply a diiusion process whose diiusivity is steered by local image properties. These lters are diicult to analyse mathematically , as they may act locally like a backward diiusion process. …

/pdf/anisotropic-diffusion-in-image-processing-z13u09qvsm.pdf

Anisotropic diffusion in image processing

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(11)60243-2.pdf

Osteoarthritis: an update with relevance for clinical practice

Thank you very much for reading statistical parametric mapping the analysis of functional brain images. As you may know, people have search numerous times for their chosen novels like this statistical parametric mapping the analysis of functional brain images, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some infectious bugs inside their desktop computer.

Statistical Parametric Mapping The Analysis Of Functional Brain Images

Blood pressure and progression of cerebral atrophy in patients with vascular disease.

Performance of knee image digital analysis of radiographs of patients with end-stage knee osteoarthritis.

High levels of angiotensin-converting-enzyme (ACE) may increase the risk of dementia through blood pressure elevation and subsequent development of cerebral small-vessel disease. However, high ACE levels may also decrease this risk through amyloid degradation which prevents brain atrophy. Within the SMART-MR study, a prospective cohort study among patients with symptomatic atherosclerotic disease, serum ACE levels were measured at baseline and a 1.5 Tesla brain MRI was performed at baseline and after on average (range) 3.9 (3.0-5.8) years of follow-up in 682 persons (mean age 58 ± 10 years). Brain segmentation was used to quantify total, deep, and periventricular white matter lesion (WML) volume, and total brain, cortical gray matter and ventricular volume (%ICV). Lacunar infarcts were rated visually. Regression analyses were used to examine the prospective associations between serum ACE and brain measures. Patients with the highest serum ACE levels (>43.3 U/L) had borderline significantly more progression of deep WML volumes than patients with the lowest ACE levels (<21.8 U/L); mean difference (95% CI) in change was 0.20 (-0.02; 0.43) %ICV. On the contrary, patients with the highest serum ACE levels had significantly less progression of cortical brain atrophy than patients with the lowest ACE levels; mean difference (95% CI) in change was 0.78 (0.21; 1.36) %ICV. Serum ACE was not associated with subcortical atrophy, periventricular WML, or lacunar infarcts. Our results show that higher ACE activity is associated with somewhat more progression of deep WML volume, but with less progression of cortical brain atrophy. This suggests both detrimental and beneficial effects of high ACE levels on the brain.

Angiotensin-Converting Enzyme and Progression of White Matter Lesions and Brain Atrophy – The SMART-MR Study

A multiscale method (the hyperstack) is proposed to segment multidimensional MR brain data. Hyperstack segmentation is based upon the linking of voxels at adjacent levels in scale space, followed by a root selection to find the voxels that represent the segments in the original image. This paper addresses an advanced linking and root labeling method for the hyperstack. In particular, attention will be paid to an extension of the linking scheme for the detection and classification of partial volume voxels. The result—a list of probabilities for each partial volume voxel—improves the resulting segmentations.

Probabilistic Hyperstack Segmentation of MR Brain Data

A hyperstack for the segmentation of 3D images.

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