Automatic extraction of abnormal regions from lung images

A system that could automatically extract abnormal lung regions may assist expert radiologists in verifying lung tissue abnormalities. This paper presents an automated lung nodule detection system consisting of five components: acquisition, pre-processing, background removal, detection, and false positives reduction. The system employs a combination of an ensemble classification and clustering methods. The performance of the developed system is compared against some existing counterparts. Based 011 the experimental results, the proposed system achieved a sensitivity of 100% and a false-positives/slice of 0.67 for 30 tested CT images.

Automated detection of lung nodules in computed tomography images: a review

Lung nodules refer to a range of lung abnormalities the detection of which can facilitate early treatment for lung patients. Lung nodules can be detected by radiologists through examining lung images. Automated detection systems that locate nodules of various sizes within lung images can assist radiologists in their decision making. This paper presents a study of the existing methods on automated lung nodule detection. It introduces a generic structure for lung nodule detection that can be used to represent and describe the existing methods. The structure consists of a number of components including: acquisition, pre-processing, lung segmentation, nodule detection, and false positives reduction. The paper describes the algorithms used to realise each component in different systems. It also provides a comparison of the performance of the existing approaches.

Processing multiple image streams for real-time monitoring of parking lots

We present a system to detect parked vehicles in a typical parking complex using multiple streams of images captured through IP connected devices. Compared to traditional object detection techniques and machine learning methods, our approach is significantly faster in detection speed in the presence of multiple image streams. It is also capable of comparable accuracy when put to test against existing methods. And this is achieved without the need to train the system that machine learning methods require. Our approach uses a combination of psychological insights obtained from human detection and an algorithm replicating the outcomes of a SVM learner but without the noise that compromises accuracy in the normal learning process. Performance enhancements are made on the algorithm so that it operates well in the context of multiple image streams. The result is faster detection with comparable accuracy. Our experiments on images captured from a local test site shows very promising results for an implementation that is not only effective and low cost but also opens doors to new parking applications when combined with other technologies.

Towards automated detection of depression from brain structural magnetic resonance images

Introduction : Depression is a major issue worldwide and is seen as a significant health problem. Stigma and patient denial, clinical experience, time limitations, and reliability of psychometrics are barriers to the clinical diagnoses of depression. Thus, the establishment of an automated system that could detect such abnormalities would assist medical experts in their decision-making process. This paper reviews existing methods for the automated detection of depression from brain structural magnetic resonance images (sMRI).Methods : Relevant sources were identified from various databases and online sites using a combination of keywords and terms including depression, major depressive disorder, detection, classification, and MRI databases. Reference lists of chosen articles were further reviewed for associated publications.Results : The paper introduces a generic structure for representing and describing the methods developed for the detection of depression from sMRI of the brain. It consists of a number of components including acquisition and preprocessing, feature extraction, feature selection, and classification.Conclusion : Automated sMRI-based detection methods have the potential to provide an objective measure of depression, hence improving the confidence level in the diagnosis and prognosis of depression.

Emotion processing fails to modulate putative mirror neuron response to trained visuomotor associations

Recent neuroimaging studies have demonstrated that activation of the putative human mirror neuron system (MNS) can be elicited via visuomotor training. This is generally interpreted as supporting an associative learning account of the mirror neuron system (MNS) that argues against the ontogeny of the MNS to be an evolutionary adaptation for social cognition. The current study assessed whether a central component of social cognition, emotion processing, would influence the MNS activity to trained visuomotor associations, which could support a broader role of the MNS in social cognition. Using functional magnetic resonance imaging (fMRI), we assessed repetition suppression to the presentation of stimulus pairs involving a simple hand action and a geometric shape that was either congruent or incongruent with earlier association training. Each pair was preceded by an image of positive, negative, or neutral emotionality. In support of an associative learning account of the MNS, repetition suppression was greater for trained pairs compared with untrained pairs in several regions, primarily supplementary motor area (SMA) and right inferior frontal gyrus (rIFG). This response, however, was not modulated by the valence of the emotional images. These findings argue against a fundamental role of emotion processing in the mirror neuron response, and are inconsistent with theoretical accounts linking mirror neurons to social cognition.

Nucleation kinetics of crystalline phases from a kaolinitic body used in the processing of red ceramics

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Fractal analysis of eroded surfaces by digital image processing

Jet impingement erosion test rig has been used to erode titanium alloy specimens (Ti-4Al-4V). Eroded surface profiles have been obtained by vertical sectioning method for light microscopy observation. Mixed fractals have been measured from profile images by a digital image processing and analysis technique. The use of this technique allows glimpsing a quantitative correlation among material properties, fractal surface topography and erosion phenomena. (C) 2002 Elsevier B.V. B.V. All rights reserved.

Quantitative characterization of multi-phase materials by digital image processing

An automatic image processing and analysis technique has been developed for quantitative characterization of multi-phase materials. For the development of this technique is used the Khoros system that offers the basic morphological tools and a flexible, visual programming language. These techniques are implemented in a highly user oriented image processing environment that allows the user to adapt each step of the processing to his special requirements.To illustrate the implementation and performance of this technique, images of two different materials are processed for microstructure characterization. The result is presented through the determination of volume fraction of the different phases or precipitates.

Quantitative fractography under light microscopy: A digital image processing approach

This work is an example of the improvement on quantitative fractography by means of digital image processing and light microscopy. Two techniques are presented to investigate the quantitative fracture behavior of Ti-4Al-4V heat-treated alloy specimens, under Charpy impact testing. The first technique is the Minkowski method for fractal dimension measurement from surface profiles, revealing the multifractal character of Ti-4Al-4V fracture. It was not observed a clear positive correlation of fractal values against Charpy energies for Ti-4Al-4V alloy specimens, due to their ductility, microstructural heterogeneities and the dynamic loading characteristics at region near the V-notch. The second technique provides an entire elevation map of fracture surface by extracting in-focus regions for each picture from a stack of images acquired at successive focus positions, then computing the surface roughness. Extended-focus reconstruction has been used to explain the behavior along fracture surface. Since these techniques are based on light microscopy, their inherent low cost is very interesting for failure investigations.

Application of wavelets to the evaluation of phantom images for mammography quality control

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

CIE L*a*b*: comparison of digital images obtained photographically by manual and automatic modes

The aim of this study was to analyze the color alterations performed by the CIE L*a*b* system in the digital imaging of shade guide tabs, which were obtained photographically according to the automatic and manual modes. This study also sought to examine the observers' agreement in quantifying the coordinates. Four Vita Lumin Vaccum shade guide tabs were used: A3.5, B1, B3 and C4. An EOS Canon digital camera was used to record the digital images of the shade tabs, and the images were processed using Adobe Photoshop software. A total of 80 observations (five replicates of each shade according to two observers in two modes, specifically, automatic and manual) were obtained, leading to color values of L*, a* and b*. The color difference (AE) between the modes was calculated and classified as either clinically acceptable or unacceptable. The results indicated that there was agreement between the two observers in obtaining the L*, a* and b* values related to all guides. However, the B1, B3, and C4 shade tabs had AE values classified as clinically acceptable (Delta E = 0.44, Delta E = 2.04 and Delta E = 2.69, respectively). The A3.5 shade tab had a AE value classified as clinically unacceptable (Delta E = 4.17), as it presented higher values for luminosity in the automatic mode (L* = 54.0) than in the manual mode (L* = 50.6). It was concluded that the B1, B3 and C4 shade tabs can be used at any of the modes in digital camera (manual or automatic), which was a different finding from that observed for the A3.5 shade tab.

Analysis of tectonic-controlled fluvial morphology and sedimentary processes of the western Amazon Basin: an approach using satellite images and digital elevation model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Strategy to detect ground control in digital images

The identification of ground control on photographs or images is usually carried out by a human operator, who uses his natural skills to make interpretations. In Digital Photogrammetry, which uses techniques of digital image processing extraction of ground control can be automated by using an approach based on relational matching and a heuristic that uses the analytical relation between straight features of object space and its homologous in the image space. A build-in self-diagnosis is also used in this method. It is based on implementation of data snooping statistic test in the process of spatial resection using the Iterated Extended Kalman Filtering (IEKF). The aim of this paper is to present the basic principles of the proposed approach and results based on real data.