Lewis D. Griffin

Zen and the art of medical image registration: correspondence, homology, and quality

Nonrigid registration (NRR) is routinely used in the study of neuroanatomy and function and is a standard component of analysis packages such as SPM. There remain many unresolved correspondence problems that arise from attempts to associate functional areas with specific neuroanatomy and to compare both function and anatomy across patient groups. Problems can result from ignorance of the underlying neurology which is then compounded by unjustified inferences drawn from the results of NRR. Usually the magnitude, distribution, and significance of errors in NRR are unknown so the errors in correspondences determined by NRR are also unknown and their effect on experimental results cannot easily be quantified. In this paper we review the principles by which the presumed correspondence and homology of structures is used to drive registration and identify the conceptual and algorithmic areas where current techniques are lacking. We suggest that for applications using NRR to be robust and achieve their potential, context-specific definitions of correspondence must be developed which properly characterise error. Prior knowledge of image content must be utilised to monitor and guide registration and gauge the degree of success. The use of NRR in voxel-based morphometry is examined from this context and found wanting. We conclude that a move away from increasingly sophisticated but context-free registration technology is required and that the veracity of studies that rely on NRR should be keenly questioned when the error distribution is unknown and the results are unsupported by other contextual information.

Using Basic Image Features for Texture Classification

Representing texture images statistically as histograms over a discrete vocabulary of local features has proven widely effective for texture classification tasks. Images are described locally by vectors of, for example, responses to some filter bank; and a visual vocabulary is defined as a partition of this descriptor-response space, typically based on clustering. In this paper, we investigate the performance of an approach which represents textures as histograms over a visual vocabulary which is defined geometrically, based on the Basic Image Features of Griffin and Lillholm (Proc. SPIE 6492(09):1–11, 2007), rather than by clustering. BIFs provide a natural mathematical quantisation of a filter-response space into qualitatively distinct types of local image structure. We also extend our approach to deal with intra-class variations in scale. Our algorithm is simple: there is no need for a pre-training step to learn a visual dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different datasets. We have tested our implementation on three popular and challenging texture datasets and find that it produces consistently good classification results on each, including what we believe to be the best reported for the KTH-TIPS and equal best reported for the UIUCTex databases.

Scale Space Methods in Computer Vision

In an ordinary 2D image the critical points and the isophotes through the saddle points provide sufficient information for classifying the image into distinct regions belonging to the extrema (i.e. a collection of bright and dark blobs), together with their nesting due to the saddle isophotes. For scale space images, obtained by convolution of the image with a Gaussian filter at a continuous range of widths for the Gaussian, things are more complicated. Here only scale space saddle points occur. They are related to spatial saddle points and spatial extrema and can thus provide a scale space based segmentation and hierarchy. However, a spatial extremum can be related to multiple scale space saddles. The key to solve this ambiguity is the investigation of both the scale space saddles and the iso-intensity manifolds (the extension of isophotes in scale space) through them. I will describe the different situations that one can encounter in this investigation, which scale space saddles are relevant, give examples and show the difference between selecting the relevant and the non-relevant (“void”) scale space saddles.

NMDA receptors regulate GABAA receptor lateral mobility and clustering at inhibitory synapses through serine 327 on the γ2 subunit.

Modification of the number of GABAA receptors (GABAARs) clustered at inhibitory synapses can regulate inhibitory synapse strength with important implications for information processing and nervous system plasticity and pathology. Currently, however, the mechanisms that regulate the number of GABAARs at synapses remain poorly understood. By imaging superecliptic pHluorin tagged GABAAR subunits we show that synaptic GABAAR clusters are normally stable, but that increased neuronal activity upon glutamate receptor (GluR) activation results in their rapid and reversible dispersal. This dispersal correlates with increases in the mobility of single GABAARs within the clusters as determined using single-particle tracking of GABAARs labeled with quantum dots. GluR-dependent dispersal of GABAAR clusters requires Ca2+ influx via NMDA receptors (NMDARs) and activation of the phosphatase calcineurin. Moreover, the dispersal of GABAAR clusters and increased mobility of individual GABAARs are dependent on serine 327 within the intracellular loop of the GABAAR γ2 subunit. Thus, NMDAR signaling, via calcineurin and a key GABAAR phosphorylation site, controls the stability of synaptic GABAARs, with important implications for activity-dependent control of synaptic inhibition and neuronal plasticity.

Superficial and deep structure in linear diffusion scale space: isophotes, critical points and separatrices

The concepts of structural stability and linear diffusion scale space are reviewed. The possible arrangements and interrelationships of isophotes, critical points and separatrices in single structurally stable images are detailed. The behaviour of these structures with changing resolution in a linear diffusion scale space is examined. This behaviour includes not only periods of smooth change, but also four catastrophic changes: shoe surface, balanced saddle, double-saddle isophote and heteroclinic separatrix.

Feature-Based Image Analysis

According to Marrs paradigm of computational vision the first process is an extraction of relevant features. The goal of this paper is to quantify and characterize the information carried by features using image-structure measured at feature-points to reconstruct images. In this way, we indirectly evaluate the concept of feature-based image analysis. The main conclusions are that (i) a reasonably low number of features characterize the image to such a high degree, that visually appealing reconstructions are possible, (ii) different feature-types complement each other and all carry important information. The strategy is to define metamery classes of images and examine the information content of a canonical least informative representative of this class. Algorithms for identifying these are given. Finally, feature detectors localizing the most informative points relative to different complexity measures derived from models of natural image statistics, are given.

Multiscale Histogram of Oriented Gradient Descriptors for Robust Character Recognition

Characters extracted from images or graphics pose a challenge for traditional character recognition techniques. The high degree of intraclass variation along with the presence of clutter makes accurate recognition difficult, yet the semantic information conveyed by sections of text within images or graphics makes their recognition an important problem. Previous work has shown that, on the two most commonly used datasets of such characters, Histogram of Oriented Gradient (HOG) descriptors have outperformed other methods. In this work we consider two extensions of the HOG descriptor to include features at multiple scales, and evaluate their performance using characters taken from images and graphics. We demonstrate that, by combining pairs of oriented gradients at different scales, its possible to achieve an increase in performance of 12.4% and 5.6% on the two datasets.

The Second Order Local-Image-Structure Solid

Characterization of second order local image structure by a 6D vector (or jet) of Gaussian derivative measurements is considered. We consider the affect on jets of a group of transformations-affine intensity-scaling, image rotation and reflection, and their compositions-that preserve intrinsic image structure. We show how this group stratifies the jet space into a system of orbits. Considering individual orbits as points, a 3D orbifold is defined. We propose a norm on jet space which we use to induce a metric on the orbifold. The metric tensor shows that the orbifold is intrinsically curved. To allow visualization of the orbifold and numerical computation with it, we present a mildly-distorting but volume-preserving embedding of it into euclidean 3-space. We call the resulting shape, which is like a flattened lemon, the second order local-image-structure solid. As an example use of the solid, we compute the distribution of local structures in noise and natural images. For noise images, analytical results are possible and they agree with the empirical results. For natural images, an excess of locally 1D structure is found.

Independent changes in female body shape with parity and age: a life-history approach to female adiposity.

Both aging and reproduction have been shown to influence female body shape in industrialized populations, involving redistribution of fat from lower to upper body regions. However, the extent to which effects of parity vary by age and the extent to which age affects shape independent of parity remain unclear. We studied shape variability in relation to age and parity in a cross‐sectional survey of 4,130 white British women, using three‐dimensional photonic scanning. In women ≤40 years, bearing children was associated with increased abdominal and reduced thigh girths, independent of age and BMI. Very few such differences were statistically significant in women >40 years, suggesting the effects of parity on shape wash out over time. In nulliparous women, aging was associated with shape variability, independent of BMI, with a similar pattern of associations evident in women both ≤40 and >40 years. Our data support previous findings of “covert maternal depletion” in relation to parity, but show that this is merely a more pronounced component of a general strategic shift of fat from lower to upper body with age. These findings are consistent with a life‐history model of female energy stores being allocated to competing “reproduction” and “maintenance” depots, with the optimal trade‐off strategy changing with age and with that strategic shift accelerated by bearing children. This model is relevant to the “grandmother hypothesis.” The dual effects of age and parity on fat distribution substantially resolve by old age the profound sexual dimorphism in adiposity present at the start of adult life. Am. J. Hum. Biol. 2010.

Automated method for the rapid and precise estimation of adherent cell culture characteristics from phase contrast microscopy images

The quantitative determination of key adherent cell culture characteristics such as confluency, morphology, and cell density is necessary for the evaluation of experimental outcomes and to provide a suitable basis for the establishment of robust cell culture protocols. Automated processing of images acquired using phase contrast microscopy (PCM), an imaging modality widely used for the visual inspection of adherent cell cultures, could enable the non‐invasive determination of these characteristics. We present an image‐processing approach that accurately detects cellular objects in PCM images through a combination of local contrast thresholding and post hoc correction of halo artifacts. The method was thoroughly validated using a variety of cell lines, microscope models and imaging conditions, demonstrating consistently high segmentation performance in all cases and very short processing times (<1 s per 1,208 × 960 pixels image). Based on the high segmentation performance, it was possible to precisely determine culture confluency, cell density, and the morphology of cellular objects, demonstrating the wide applicability of our algorithm for typical microscopy image processing pipelines. Furthermore, PCM image segmentation was used to facilitate the interpretation and analysis of fluorescence microscopy data, enabling the determination of temporal and spatial expression patterns of a fluorescent reporter. We created a software toolbox (PHANTAST) that bundles all the algorithms and provides an easy to use graphical user interface. Source‐code for MATLAB and ImageJ is freely available under a permissive open‐source license. Biotechnol. Bioeng. 2014;111: 504–517.