Processing biases in eating disorders: the impact of temporal factors

Objective: Previous research has indicated that temporal factors [specifically, the duration of interstimulus intervals (ISI) during a threat processing task] may influence the nature of processing biases exhibited in nonclinical populations with some degree of eating disorder psychopathology (Meyer et al., Int J Eat Disord, 27, 405-410, 2000). The current study aimed to test this hypothesis by investigating attentional biases for eating-disorder-relevant images and irrelevant visual images (animals) in patients with eating disorders (n = 23) and psychiatric (n = 19) and nonpsychiatric (n = 65) controls. Method: A dot probe task was modified from previous research (Shafran et al., Int Eat Disord, 40, 369-380, 2007), whereby an original ISI of 500 ms was increased to 2.000 ms. Results: Patients with an eating disorder continued to display a bias in the processing of weight stimuli. However, biases noted in previous research for shape and weight stimuli disappeared when the ISI duration was increased in this way. Conclusion: These findings highlight the importance of temporal factors in whether processing biases are displayed and may point to ways in which biases actually work in this population. However, further research is warranted. (C) 2008 by Wiley Periodicals, Inc.

The impact of novel labels on visual processing during infancy

The impact of novel labels on visual processing was investigated across two experiments with infants aged between 9 and 21 months. Infants viewed pairs of images across a series of preferential looking trials. On each trial, one image was novel, and the other image had previously been viewed by the infant. Some infants viewed images in silence; other infants viewed images accompanied by novel labels. The pattern of fixations both across and within trials revealed that infants in the labelling condition took longer to develop a novelty preference than infants in the silent condition. Our findings contrast with prior research by Robinson and Sloutsky (e.g., Robinson & Sloutsky, 2007a; Sloutsky & Robinson, 2008) who found that novel labels did not disrupt visual processing for infants aged over a year. Provided that overall task demands are sufficiently high, it appears that labels can disrupt visual processing for infants during the developmental period of establishing a lexicon. The results suggest that when infants are processing labels and objects, attentional resources are shared across modalities.

Investigating subtypes of reward processing deficits as trait markers for depression

Background: Anhedonia, the loss of pleasure in usually enjoyable activities, is a central feature of major depressive disorder (MDD). The aim of the present study was to examine whether young people at a familial risk of depression display signs of anticipatory, motivational or consummatory anhedonia, which would indicate that these deficits may be trait markers for MDD. Methods: The study was completed by 22 participants with a family history of depression (FH+) and 21 controls (HC). Anticipatory anhedonia was assessed by asking participants to rate their anticipated liking of pleasant and unpleasant foods which they imagined tasting when cued with images of the foods. Motivational anhedonia was measured by requiring participants to perform key presses to obtain pleasant chocolate taste rewards or to avoid unpleasant apple tastes. Additionally, physical consummatory anhedonia was examined by instructing participants to rate the pleasantness of the acquired tastes. Moreover, social consummatory anhedonia was investigated by asking participants to make preference-based choices between neutral facial expressions, genuine smiles, and polite smiles. Results: It was found that the FH+ group’s anticipated liking of unpleasant foods was significantly lower than that of the control group. By contrast, no group differences in the pleasantness ratings of the actually experienced tastes or in the amount of performed key presses were observed. However, controls preferred genuine smiles over neutral expressions more often than they preferred polite smiles over neutral expressions, while this pattern was not seen in the FH+ group. Conclusion: These findings suggest that FH+ individuals demonstrate an altered anticipatory response to negative stimuli and show signs of social consummatory anhedonia, which may be trait markers for depression.

MODELING VERY LONG BASELINE INTERFEROMETRIC IMAGES WITH THE CROSS-ENTROPY GLOBAL OPTIMIZATION TECHNIQUE

We present a new technique for obtaining model fittings to very long baseline interferometric images of astrophysical jets. The method minimizes a performance function proportional to the sum of the squared difference between the model and observed images. The model image is constructed by summing N(s) elliptical Gaussian sources characterized by six parameters: two-dimensional peak position, peak intensity, eccentricity, amplitude, and orientation angle of the major axis. We present results for the fitting of two main benchmark jets: the first constructed from three individual Gaussian sources, the second formed by five Gaussian sources. Both jets were analyzed by our cross-entropy technique in finite and infinite signal-to-noise regimes, the background noise chosen to mimic that found in interferometric radio maps. Those images were constructed to simulate most of the conditions encountered in interferometric images of active galactic nuclei. We show that the cross-entropy technique is capable of recovering the parameters of the sources with a similar accuracy to that obtained from the very traditional Astronomical Image Processing System Package task IMFIT when the image is relatively simple (e. g., few components). For more complex interferometric maps, our method displays superior performance in recovering the parameters of the jet components. Our methodology is also able to show quantitatively the number of individual components present in an image. An additional application of the cross-entropy technique to a real image of a BL Lac object is shown and discussed. Our results indicate that our cross-entropy model-fitting technique must be used in situations involving the analysis of complex emission regions having more than three sources, even though it is substantially slower than current model-fitting tasks (at least 10,000 times slower for a single processor, depending on the number of sources to be optimized). As in the case of any model fitting performed in the image plane, caution is required in analyzing images constructed from a poorly sampled (u, v) plane.

Generating Segmented Quality Meshes from Images

Techniques devoted to generating triangular meshes from intensity images either take as input a segmented image or generate a mesh without distinguishing individual structures contained in the image. These facts may cause difficulties in using such techniques in some applications, such as numerical simulations. In this work we reformulate a previously developed technique for mesh generation from intensity images called Imesh. This reformulation makes Imesh more versatile due to an unified framework that allows an easy change of refinement metric, rendering it effective for constructing meshes for applications with varied requirements, such as numerical simulation and image modeling. Furthermore, a deeper study about the point insertion problem and the development of geometrical criterion for segmentation is also reported in this paper. Meshes with theoretical guarantee of quality can also be obtained for each individual image structure as a post-processing step, a characteristic not usually found in other methods. The tests demonstrate the flexibility and the effectiveness of the approach.

Topological triangle characterization with application to object detection from images

A novel mathematical framework inspired on Morse Theory for topological triangle characterization in 2D meshes is introduced that is useful for applications involving the creation of mesh models of objects whose geometry is not known a priori. The framework guarantees a precise control of topological changes introduced as a result of triangle insertion/removal operations and enables the definition of intuitive high-level operators for managing the mesh while keeping its topological integrity. An application is described in the implementation of an innovative approach for the detection of 2D objects from images that integrates the topological control enabled by geometric modeling with traditional image processing techniques. (C) 2008 Published by Elsevier B.V.

Automatic contour extraction from 2D neuron images

This work describes a novel methodology for automatic contour extraction from 2D images of 3D neurons (e.g. camera lucida images and other types of 2D microscopy). Most contour-based shape analysis methods cannot be used to characterize such cells because of overlaps between neuronal processes. The proposed framework is specifically aimed at the problem of contour following even in presence of multiple overlaps. First, the input image is preprocessed in order to obtain an 8-connected skeleton with one-pixel-wide branches, as well as a set of critical regions (i.e., bifurcations and crossings). Next, for each subtree, the tracking stage iteratively labels all valid pixel of branches, tip to a critical region, where it determines the suitable direction to proceed. Finally, the labeled skeleton segments are followed in order to yield the parametric contour of the neuronal shape under analysis. The reported system was successfully tested with respect to several images and the results from a set of three neuron images are presented here, each pertaining to a different class, i.e. alpha, delta and epsilon ganglion cells, containing a total of 34 crossings. The algorithms successfully got across all these overlaps. The method has also been found to exhibit robustness even for images with close parallel segments. The proposed method is robust and may be implemented in an efficient manner. The introduction of this approach should pave the way for more systematic application of contour-based shape analysis methods in neuronal morphology. (C) 2008 Elsevier B.V. All rights reserved.

Machine vision for the automatic classification of images acquired from Non-destructive tests

This project is based on Artificial Intelligence (A.I) and Digital Image processing (I.P) for automatic condition monitoring of sleepers in the railway track. Rail inspection is a very important task in railway maintenance for traffic safety issues and in preventing dangerous situations. Monitoring railway track infrastructure is an important aspect in which the periodical inspection of rail rolling plane is required.Up to the present days the inspection of the railroad is operated manually by trained personnel. A human operator walks along the railway track searching for sleeper anomalies. This monitoring way is not more acceptable for its slowness and subjectivity. Hence, it is desired to automate such intuitive human skills for the development of more robust and reliable testing methods. Images of wooden sleepers have been used as data for my project. The aim of this project is to present a vision based technique for inspecting railway sleepers (wooden planks under the railway track) by automatic interpretation of Non Destructive Test (NDT) data using A.I. techniques in determining the results of inspection.

Image processing technique to count the number of logs in a timber truck

This paper summarises the results of using image processing technique to get information about the load of timber trucks before their arrival using digital images or geo tagged images. Once the images are captured and sent to sawmill by drivers from forest, we can predict their arrival time using geo tagged coordinates, count the number of (timber) logs piled up in a truck, identify their type and calculate their diameter. With this information we can schedule and prioritise the inflow and unloading of trucks in the light of production schedules and raw material stocks available at the sawmill yard. It is important to keep all the actors in a supply chain integrated coordinated, so that optimal working routines can be reached in the sawmill yard.   

Industrial application of thermal image processing and thermal control

Industrial application of infrared thermography is virtually boundless as it can be used in any situations where there are temperature differences. This technology has particularly been widely used in automotive industry for process evaluation and system design. In this work, thermal image processing technique will be introduced to quantitatively calculate the heat stored in a warm/hot object and consequently, a thermal control system will be proposed to accurately and actively manage the thermal distribution within the object in accordance with the heat calculated from the thermal images.

Object extraction using genetic algorithms and morphological processing

This paper introduces a novel methodology for object detection using genetic algorithms and morphological processing. The method employs a kind of object oriented structuring element, which are derived using genetic algorithm operating. The population of morphological filters iteratively evaluated according to a statistical performance index corresponding to object extraction ability, and evolves into an optimal structuring elements using the evolution principles of genetic search. Experimental results of object extraction in high resolution satellite images are presented to illustrate the merit and feasibility of the proposed method.

From lung images to lung models : a review

Automated 3D lung modeling involves analyzing 2D lung images and reconstructing a realistic 3D model of the lung. This paper presents a review of the existing works on automatic formation of 3D lung models from 2D lung images. A common framework for 3D lung modeling is proposed. It consists of eight components: image acquisition, image pre-processing, image segmentation, boundary creation, image recognition, image registration, 3D surface reconstruction, and 3D rendering and visualization. The algorithms used by the existing systems to implement these components are also reviewed.

New methods for objective evaluation of fabric pilling by frequency domain image processing

Fabric pilling is a serious problem for the apparel industry. Resistance to pilling is normally tested by simulated accelerated wear and manual assessment of degree of pilling based on a visual comparison of the sample to a set of test images. A number of automated systems based on image analysis have been developed. The authors propose new methods of image analysis based on the two-dimensional wavelet transform to objectively measure the pilling intensity in sample images. Initial work employed the detail coefficients of the two-dimensional discrete wavelet transform (2DDWT) as a measure of the pilling intensity of woven/knitted fabrics.

This method is shown to be robust to image translation and brightness variation. Using the approximation coefficients of the 2DDWT, the method is extended to non-woven pilling image sets. Wavelet texture analysis (WTA) combined with principal components analysis are shown to produce a richer texture description of pilling for analysis and classification. Finally, employing the two-dimensional dual-tree complex wavelet transform as the basis for the WTA feature vector is shown to produce good automated classification on a range of standard pilling image sets.

Automatic detection of lung nodule from computer tomography images

Lung nodule refers to lung tissue abnormalities that may become cancerous. An automated system that detects nodules of common sizes within lung images is developed. It consists of acquisition, pre-processing, background removal, nodule detection, and false positives reduction. The system can assist expert radiologists in their decision making.