Biomedical subjects

Lewis D Griffin

Publications and source records attributed to Lewis D Griffin.

5 recordsLinked to original sources

PubMed2006

Optimality of the basic colour categories for classification.

Categorization of colour has been widely studied as a window into human language and cognition, and quite separately has been used pragmatically in image-database retrieval systems. This suggests the hypothesis that the best category system for pragmatic purposes coincides with human categories (i.e. the basic colours). We have tested this hypothesis by assessing the performance of different category systems in a machine-vision task. The task was the identification of the odd-one-out from triples of images obtained using a web-based image-search service. In each triple, two of the images had been retrieved using the same search term, the other a different term. The terms were simple concrete nouns. The results were as follows: (i) the odd-one-out task can be performed better than chance using colour alone; (ii) basic colour categorization performs better than random systems of categories; (iii) a category system that performs better than the basic colours could not be found; and (iv) it is not just the general layout of the basic colours that is important, but also the detail. We conclude that (i) the results support the plausibility of an explanation for the basic colours as a result of a pressure-to-optimality and (ii) the basic colours are good categories for machine vision image-retrieval systems.

Algorithms↗

PubMed2004

Natural image profiles are most likely to be step edges.

We introduce Geometric Texton Theory (GTT), a theory of categorical visual feature classification that arises through consideration of the metamerism that affects families of co-localised linear receptive-field operators. A refinement of GTT that uses maximum likelihood (ML) to resolve this metamerism is presented. We describe a method for discovering the ML element of a metamery class by analysing a database of natural images. We apply the method to the simplest case--the ML element of a canonical metamery class defined by co-registering the location and orientation of profiles from images, and affinely scaling their intensities so that they have identical responses to 1-D, zeroth- and first-order, derivative of Gaussian operators. We find that a step edge is the ML profile. This result is consistent with our proposed theory of feature classification.

Databases, Factual↗

PubMed2002

Spatial normalization and averaging of diffusion tensor MRI data sets.

Diffusion tensor magnetic resonance imaging (DT-MRI) is unique in providing information about both the structural integrity and the orientation of white matter fibers in vivo and, through "tractography", revealing the trajectories of white matter tracts. DT-MRI is therefore a promising technique for detecting differences in white matter architecture between different subject populations. However, while studies involving analyses of group averages of scalar quantities derived from DT-MRI data have been performed, as yet there have been no similar studies involving the whole tensor. Here we present the first step towards realizing such a study, i.e., the spatial normalization of whole tensor data sets. The approach is illustrated by spatial normalization of 10 DT-MRI data sets to a standard anatomical template. Both qualitative and quantitative approaches are described for assessing the results of spatial normalization. Techniques are then described for combining the spatially normalized data sets according to three definitions of average, i.e., the mean, median, and mode of a distribution of tensors. The current absence of, and hence need for, appropriate statistical tests for comparison of results derived from group-averaged DT-MRI data sets is then discussed. Finally, the feasibility of performing tractography on the group-averaged DT-MRI data set is investigated and the possibility and implications of generating a generic map of brain connectivity from a group of subjects is considered.

Adult↗

PubMedDate not supplied

A 3D fiber model of the human brainstem.

A new neuroanatomic approach to evaluate the fiber orientation in gross histological sections of the human brain was developed. Serial sections of a human brainstem were used to derive fiber orientation maps by analysis of polarized light sequences of these sections. Fiber inclination maps visualize angles of inclination, and fiber direction maps show angles of direction. These angles define vectors which can be visualized as RGB-colors. The serial sections were aligned to each other using the minimized Euclidian distance as fit criterion. In the 3D data set of the human brainstem the major fiber tracts were segmented, and three-dimensional models of these fiber tracts were generated. The presented results demonstrate that two kinds of fiber atlases are feasible: a fiber orientation atlas representing a vector in each voxel, which shows the nerve fiber orientation, and a volume-based atlas representing the major fiber tracts. These models can be used for the evaluation of diffusion tensor data as well as for neurosurgical planning.

Aged↗

PubMedDate not supplied

Feature classes for 1D, 2nd order image structure arise from natural image maximum likelihood statistics.

Much is understood of how quantitative aspects of image structure are measured by VI simple cells, but less about how qualitative structure is determined from these measurements. We review Geometric Texton Theory (GTT) that aims to describe this step from quantitative to qualitative. GTT proposes that qualitative feature categories arise through consideration of the maximum likelihood (ML) explanations of image measurements. It posits that a pair of output vectors of an ensemble of co-localised neurons signal the same feature category if and only if the corresponding ML explanations are qualitatively similar. We present mathematical and empirical results relevant to GTT for the limited case of measurement by 1D filters of up to 2nd order. The mathematical results identify the simplest explanations for measurements by such filters, while the empirical results identify the ML. We find that the ML explanations are not the most simple under any of the definitions of simple that we examined. However, the ML explanations do have properties predicted by GTT. In particular they change rapidly and qualitatively for certain narrow regions of measurement space, while remaining qualitative stable between those transition regions. Three feature categories arise naturally from the data: light bars, dark bars and edges. The results are consistent with GTT.

Animals↗