A collaborative approach for incorporating forensic case data into crime investigation using criminal intelligence analysis and visualisation

There is an increasing awareness that the articulation of forensic science and criminal investigation is critical to the resolution of crimes. However, models and methods to support an effective collaboration between these partners are still poorly expressed or even lacking. Three propositions are borrowed from crime intelligence methods in order to bridge this gap: (a) the general intelligence process, (b) the analyses of investigative problems along principal perspectives: entities and their relationships, time and space, quantitative aspects and (c) visualisation methods as a mode of expression of a problem in these dimensions. Indeed, in a collaborative framework, different kinds of visualisations integrating forensic case data can play a central role for supporting decisions. Among them, link-charts are scrutinised for their abilities to structure and ease the analysis of a case by describing how relevant entities are connected. However, designing an informative chart that does not bias the reasoning process is not straightforward. Using visualisation as a catalyser for a collaborative approach integrating forensic data thus calls for better specifications.

Research in psychopathology: epistemologic issues.

Etiologic research in psychiatry relies on an objectivist epistemology positing that human cognition is specified by the "reality" of the outer world, which consists of a totality of mind-independent objects. Truth is considered as some sort of correspondence relation between words and external objects, and mind as a mirror of nature. In our view, this epistemology considerably impedes etiologic research. Objectivist epistemology has been recently confronting a growing critique from diverse scientific fields. Alternative models in neurosciences (neuronal selection), artificial intelligence (connectionism), and developmental psychology (developmental biodynamics) converge in viewing living organisms as self-organizing systems. In this perspective, the organism is not specified by the outer world, but enacts its environment by selecting relevant domains of significance that constitute its world. The distinction between mind and body or organism and environment is a matter of observational perspective. These models from empirical sciences are compatible with fundamental tenets of philosophical phenomenology and hermeneutics. They imply consequences for research in psychopathology: symptoms cannot be viewed as disconnected manifestations of discrete localized brain dysfunctions. Psychopathology should therefore focus on how the person's self-coherence is maintained and on the understanding and empirical investigation of the systemic laws that govern neurodevelopment and the organization of human cognition.

Representing diffusion MRI in 5D for segmentation of white matter tracts with a level set method.

We present a method for segmenting white matter tracts from high angular resolution diffusion MR. images by representing the data in a 5 dimensional space of position and orientation. Whereas crossing fiber tracts cannot be separated in 3D position space, they clearly disentangle in 5D position-orientation space. The segmentation is done using a 5D level set method applied to hyper-surfaces evolving in 5D position-orientation space. In this paper we present a methodology for constructing the position-orientation space. We then show how to implement the standard level set method in such a non-Euclidean high dimensional space. The level set theory is basically defined for N-dimensions but there are several practical implementation details to consider, such as mean curvature. Finally, we will show results from a synthetic model and a few preliminary results on real data of a human brain acquired by high angular resolution diffusion MRI.

A multidimensional segmentation evaluation for medical image data.

Evaluation of segmentation methods is a crucial aspect in image processing, especially in the medical imaging field, where small differences between segmented regions in the anatomy can be of paramount importance. Usually, segmentation evaluation is based on a measure that depends on the number of segmented voxels inside and outside of some reference regions that are called gold standards. Although some other measures have been also used, in this work we propose a set of new similarity measures, based on different features, such as the location and intensity values of the misclassified voxels, and the connectivity and the boundaries of the segmented data. Using the multidimensional information provided by these measures, we propose a new evaluation method whose results are visualized applying a Principal Component Analysis of the data, obtaining a simplified graphical method to compare different segmentation results. We have carried out an intensive study using several classic segmentation methods applied to a set of MRI simulated data of the brain with several noise and RF inhomogeneity levels, and also to real data, showing that the new measures proposed here and the results that we have obtained from the multidimensional evaluation, improve the robustness of the evaluation and provides better understanding about the difference between segmentation methods.

Ambulatory monitoring of physical activities in patients with Parkinson's disease.

A new ambulatory method of monitoring physical activities in Parkinson's disease (PD) patients is proposed based on a portable data-logger with three body-fixed inertial sensors. A group of ten PD patients treated with subthalamic nucleus deep brain stimulation (STN-DBS) and ten normal control subjects followed a protocol of typical daily activities and the whole period of the measurement was recorded by video. Walking periods were recognized using two sensors on shanks and lying periods were detected using a sensor on trunk. By calculating kinematics features of the trunk movements during the transitions between sitting and standing postures and using a statistical classifier, sit-to-stand (SiSt) and stand-to-sit (StSi) transitions were detected and separated from other body movements. Finally, a fuzzy classifier used this information to detect periods of sitting and standing. The proposed method showed a high sensitivity and specificity for the detection of basic body postures allocations: sitting, standing, lying, and walking periods, both in PD patients and healthy subjects. We found significant differences in parameters related to SiSt and StSi transitions between PD patients and controls and also between PD patients with and without STN-DBS turned on. We concluded that our method provides a simple, accurate, and effective means to objectively quantify physical activities in both normal and PD patients and may prove useful to assess the level of motor functions in the latter.

S-system parameter estimation for noisy metabolic profiles using newton-flow analysis.

Biochemical systems are commonly modelled by systems of ordinary differential equations (ODEs). A particular class of such models called S-systems have recently gained popularity in biochemical system modelling. The parameters of an S-system are usually estimated from time-course profiles. However, finding these estimates is a difficult computational problem. Moreover, although several methods have been recently proposed to solve this problem for ideal profiles, relatively little progress has been reported for noisy profiles. We describe a special feature of a Newton-flow optimisation problem associated with S-system parameter estimation. This enables us to significantly reduce the search space, and also lends itself to parameter estimation for noisy data. We illustrate the applicability of our method by applying it to noisy time-course data synthetically produced from previously published 4- and 30-dimensional S-systems. In addition, we propose an extension of our method that allows the detection of network topologies for small S-systems. We introduce a new method for estimating S-system parameters from time-course profiles. We show that the performance of this method compares favorably with competing methods for ideal profiles, and that it also allows the determination of parameters for noisy profiles.

Bayesian networks for evaluating forensic DNA profiling evidence: a review and guide to literature

Almost 30 years ago, Bayesian networks (BNs) were developed in the field of artificial intelligence as a framework that should assist researchers and practitioners in applying the theory of probability to inference problems of more substantive size and, thus, to more realistic and practical problems. Since the late 1980s, Bayesian networks have also attracted researchers in forensic science and this tendency has considerably intensified throughout the last decade. This review article provides an overview of the scientific literature that describes research on Bayesian networks as a tool that can be used to study, develop and implement probabilistic procedures for evaluating the probative value of particular items of scientific evidence in forensic science. Primary attention is drawn here to evaluative issues that pertain to forensic DNA profiling evidence because this is one of the main categories of evidence whose assessment has been studied through Bayesian networks. The scope of topics is large and includes almost any aspect that relates to forensic DNA profiling. Typical examples are inference of source (or, 'criminal identification'), relatedness testing, database searching and special trace evidence evaluation (such as mixed DNA stains or stains with low quantities of DNA). The perspective of the review presented here is not exclusively restricted to DNA evidence, but also includes relevant references and discussion on both, the concept of Bayesian networks as well as its general usage in legal sciences as one among several different graphical approaches to evidence evaluation.

Inherently self-calibrating non-Cartesian parallel imaging.

The use of self-calibrating techniques in parallel magnetic resonance imaging eliminates the need for coil sensitivity calibration scans and avoids potential mismatches between calibration scans and subsequent accelerated acquisitions (e.g., as a result of patient motion). Most examples of self-calibrating Cartesian parallel imaging techniques have required the use of modified k-space trajectories that are densely sampled at the center and more sparsely sampled in the periphery. However, spiral and radial trajectories offer inherent self-calibrating characteristics because of their densely sampled center. At no additional cost in acquisition time and with no modification in scanning protocols, in vivo coil sensitivity maps may be extracted from the densely sampled central region of k-space. This work demonstrates the feasibility of self-calibrated spiral and radial parallel imaging using a previously described iterative non-Cartesian sensitivity encoding algorithm.

Atlas-based segmentation of pathological MR brain images using a model of lesion growth.

We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.

A surface-based approach to quantify local cortical gyrification.

The high complexity of cortical convolutions in humans is very challenging both for engineers to measure and compare it, and for biologists and physicians to understand it. In this paper, we propose a surface-based method for the quantification of cortical gyrification. Our method uses accurate 3-D cortical reconstruction and computes local measurements of gyrification at thousands of points over the whole cortical surface. The potential of our method to identify and localize precisely gyral abnormalities is illustrated by a clinical study on a group of children affected by 22q11 Deletion Syndrome, compared to control individuals.

ZHARP: three-dimensional motion tracking from a single image plane.

Three-dimensional imaging and quantification of myocardial function are essential steps in the evaluation of cardiac disease. We propose a tagged magnetic resonance imaging methodology called zHARP that encodes and automatically tracks myocardial displacement in three dimensions. Unlike other motion encoding techniques, zHARP encodes both in-plane and through-plane motion in a single image plane without affecting the acquisition speed. Postprocessing unravels this encoding in order to directly track the 3-D displacement of every point within the image plane throughout an entire image sequence. Experimental results include a phantom validation experiment, which compares zHARP to phase contrast imaging, and an in vivo study of a normal human volunteer. Results demonstrate that the simultaneous extraction of in-plane and through-plane displacements from tagged images is feasible.

Optimization of snowmaking in high mountains ski resort

Ski resorts are deploying more and more systems of artificial snow. These tools are necessary to ensure an important economic activity for the high alpine valleys. However, artificial snow raises important environmental issues that can be reduced by an optimization of its production. This paper presents a software prototype based on artificial intelligence to help ski resorts better manage their snowpack. It combines on one hand a General Neural Network for the analysis of the snow cover and the spatial prediction, with on the other hand a multiagent simulation of skiers for the analysis of the spatial impact of ski practice. The prototype has been tested on the ski resort of Verbier (Switzerland).

Intelligence-led crime scene processing. Part I : Forensic intelligence.

Forensic science is generally defined as the application of science to address questions related to the law. Too often, this view restricts the contribution of science to one single process which eventually aims at bringing individuals to court while minimising risk of miscarriage of justice. In order to go beyond this paradigm, we propose to refocus the attention towards traces themselves, as remnants of a criminal activity, and their information content. We postulate that traces contribute effectively to a wide variety of other informational processes that support decision making inmany situations. In particular, they inform actors of new policing strategies who place the treatment of information and intelligence at the centre of their systems. This contribution of forensic science to these security oriented models is still not well identified and captured. In order to create the best condition for the development of forensic intelligence, we suggest a framework that connects forensic science to intelligence-led policing (part I). Crime scene attendance and processing can be envisaged within this view. This approach gives indications abouthowto structure knowledge used by crime scene examiners in their effective practice (part II).