Improving accuracy and efficiency of mutual information for multi-modal retinal image registration using adaptive probability density estimation

Mutual information (MI) is a popular similarity measure for performing image registration between different modalities. MI makes a statistical comparison between two images by computing the entropy from the probability distribution of the data. Therefore, to obtain an accurate registration it is important to have an accurate estimation of the true underlying probability distribution. Within the statistics literature, many methods have been proposed for finding the 'optimal' probability density, with the aim of improving the estimation by means of optimal histogram bin size selection. This provokes the common question of how many bins should actually be used when constructing a histogram. There is no definitive answer to this. This question itself has received little attention in the MI literature, and yet this issue is critical to the effectiveness of the algorithm. The purpose of this paper is to highlight this fundamental element of the MI algorithm. We present a comprehensive study that introduces methods from statistics literature and incorporates these for image registration. We demonstrate this work for registration of multi-modal retinal images: colour fundus photographs and scanning laser ophthalmoscope images. The registration of these modalities offers significant enhancement to early glaucoma detection, however traditional registration techniques fail to perform sufficiently well. We find that adaptive probability density estimation heavily impacts on registration accuracy and runtime, improving over traditional binning techniques. © 2013 Elsevier Ltd

Challenges associated with x-ray imaging of stretcher-bound patients

Patients often arrive at imaging departments on stretchers, and in certain circumstances they must remain on the stretcher for the imaging examination to reduce the likelihood of exacerbating injuries. Imaging stretcher-bound patients can be challenging, with many physical and technical variables to consider. These challenges occur because of differences between imaging a patient on a tabletop and imaging a patient on a stretcher. This article reviews the issues associated with imaging stretcher- bound patients, including the unavailability of the automatic exposure control, different grids used, geometric factors, and variability in stretcher design

Spatial Analysis of Temporal Changes in the Pandemic of Severe Cassava Mosaic Disease in Northwestern Tanzania

Published online: 8 Sept 2017To improve understanding of the dynamics of the cassava mosaic disease (CMD) pandemic front, geospatial approaches were applied to the analysis of 3 years’ data obtained from a 2-by-2° (approximately 222-by-222 km) area of northwestern Tanzania. In total, 80 farmers’ fields were assessed in each of 2009, 2010, and 2011, with 20 evenly distributed fields per 1-by-1° quadrant. CMD-associated variables (CMD incidence, CMD severity, vector-borne CMD infection, and vector abundance) increased in magnitude from 2009 to 2010 but showed little change from 2010 to 2011. Increases occurred primarily in the two westernmost quadrants of the study area. A pandemic “front” was defined by determining the values of CMD incidence and whitefly abundance where predicted disease gradients were greatest. The pandemic-associated virus (East African cassava mosaic virus-Uganda) and vector genotype (Bemisia tabaci sub-Saharan Africa 1–subgroup 1) were both present within the area bounded by the CMD incidence front but both also occurred ahead of the front. The average speed and direction of movement of the CMD incidence front (22.9 km/year; southeast) and whitefly abundance front (46.6 km/year; southeast) were calculated, and production losses due to CMD were estimated to range from US$4.3 million to 12.2 million

Superconductivity in an Einstein Solid AxV2Al20 (A = Al and Ga)

A cage compound AxV2Al20 (Al10V), that was called an Einstein solid by Caplin and coworkers 40 years ago, is revisited to investigate the low-energy, local vibrations of the A atoms and their influence on the electronic and superconducting properties of the compound. Polycrystalline samples with A = Al, Ga, Y, and La are studied through resistivity and heat capacity measurements. Weak-coupling BCS superconductivity is observed below Tc = 1.49, 1.66, and 0.69 K for Ax = Al0.3, Ga0.2, and Y, respectively, but not above 0.4 K for Ax = La. Low-energy modes are detected only for A = Al and Ga, which are approximately described by the Einstein model with Einstein temperatures of 24 and 8 K, respectively. A weak but significant coupling between the low-energy modes, which are almost identical to those called rattling in recent study, and conduction electrons manifests itself as anomalous enhancement in resistivity at around low temperatures corresponding to the Einstein temperatures.Comment: 12 pages, 5 figures, to be published in J. Phys. Soc. Jp

Anisotropy of the space orientation of radio sources. I: The catalog

A catalog of the extended extragalactic radio sources consisting of 10461 objects is compiled based on the list of radio sources of the FIRST survey. A total of 1801 objects are identified with galaxies and quasars of the SDSS survey and the Veron-Veron catalog. The distribution of the position angles of the axes of radio sources from the catalog is determined, and the probability that this distribution is equiprobable is shown to be less then 10^(-7). This result implies that at Z equal to or smaller then 0.5, spatial orientation of the axes of radio sources is anisotropic at a statistically significant level.Comment: 8 pages, 7 figure

Visual analytics for collaborative human-machine confidence in human-centric active learning tasks

Active machine learning is a human-centric paradigm that leverages a small labelled dataset to build an initial weak classifier, that can then be improved over time through human-machine collaboration. As new unlabelled samples are observed, the machine can either provide a prediction, or query a human ‘oracle’ when the machine is not confident in its prediction. Of course, just as the machine may lack confidence, the same can also be true of a human ‘oracle’: humans are not all-knowing, untiring oracles. A human’s ability to provide an accurate and confident response will often vary between queries, according to the duration of the current interaction, their level of engagement with the system, and the difficulty of the labelling task. This poses an important question of how uncertainty can be expressed and accounted for in a human-machine collaboration. In short, how can we facilitate a mutually-transparent collaboration between two uncertain actors - a person and a machine - that leads to an improved outcome?In this work, we demonstrate the benefit of human-machine collaboration within the process of active learning, where limited data samples are available or where labelling costs are high. To achieve this, we developed a visual analytics tool for active learning that promotes transparency, inspection, understanding and trust, of the learning process through human-machine collaboration. Fundamental to the notion of confidence, both parties can report their level of confidence during active learning tasks using the tool, such that this can be used to inform learning. Human confidence of labels can be accounted for by the machine, the machine can query for samples based on confidence measures, and the machine can report confidence of current predictions to the human, to further the trust and transparency between the collaborative parties. In particular, we find that this can improve the robustness of the classifier when incorrect sample labels are provided, due to unconfidence or fatigue. Reported confidences can also better inform human-machine sample selection in collaborative sampling. Our experimentation compares the impact of different selection strategies for acquiring samples: machine-driven, human-driven, and collaborative selection. We demonstrate how a collaborative approach can improve trust in the model robustness, achieving high accuracy and low user correction, with only limited data sample selections

Cassava whitefly, Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae), in sub-Saharan African farming landscapes: a review of the factors determining abundance

Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is a pest species complex that causes widespread damage to cassava, a staple food crop for millions of smallholder households in Sub-Saharan Africa. Species in the complex cause direct feeding damage to cassava and are the vectors of multiple plant viruses. Whilst significant work has gone into developing virus-resistant cassava cultivars, there has been little research effort aimed at understanding the ecology of these insect vectors. In this review we critically assess the knowledge base relating to factors that may lead to high population densities of Sub-Saharan African (SSA) Bemisia tabaci species in cassava production landscapes of East Africa. We focus first on empirical studies that have examined biotic or abiotic factors that may lead to high populations. We then identify knowledge gaps that need to be filled to deliver long-term sustainable solutions to manage both the vectors and the viruses that they transmit. We found that whilst many hypotheses have been put forward to explain the increases in abundance witnessed since the early 1990s, there are little available published data and these tend to have been collected in a piecemeal manner. The most critical knowledge gaps identified were: (i) understanding how cassava cultivars and alternative host plants impact B. tabaci population dynamics and its natural enemies; (ii) the impact of natural enemies in terms of reducing the frequency of outbreaks and (iii) the use and management of insecticides to delay or avoid the development of resistance. In addition, there are several fundamental methodologies that need to be developed and deployed in East Africa to address some of the more challenging knowledge gaps