Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI

The structural connectivity of the brain is considered to encode species-wise and subject-wise patterns that will unlock large areas of understanding of the human brain. Currently, diffusion MRI of the living brain enables to map the microstructure of tissue, allowing to track the pathways of fiber bundles connecting the cortical regions across the brain. These bundles are summarized in a network representation called connectome that is analyzed using graph theory. The extraction of the connectome from diffusion MRI requires a large processing flow including image enhancement, reconstruction, segmentation, registration, diffusion tracking, etc. Although a concerted effort has been devoted to the definition of standard pipelines for the connectome extraction, it is still crucial to define quality assessment protocols of these workflows. The definition of quality control protocols is hindered by the complexity of the pipelines under test and the absolute lack of gold-standards for diffusion MRI data. Here we characterize the impact on structural connectivity workflows of the geometrical deformation typically shown by diffusion MRI data due to the inhomogeneity of magnetic susceptibility across the imaged object. We propose an evaluation framework to compare the existing methodologies to correct for these artifacts including whole-brain realistic phantoms. Additionally, we design and implement an image segmentation and registration method to avoid performing the correction task and to enable processing in the native space of diffusion data. We release PySDCev, an evaluation framework for the quality control of connectivity pipelines, specialized in the study of susceptibility-derived distortions. In this context, we propose Diffantom, a whole-brain phantom that provides a solution to the lack of gold-standard data. The three correction methodologies under comparison performed reasonably, and it is difficult to determine which method is more advisable. We demonstrate that susceptibility-derived correction is necessary to increase the sensitivity of connectivity pipelines, at the cost of specificity. Finally, with the registration and segmentation tool called regseg we demonstrate how the problem of susceptibility-derived distortion can be overcome allowing data to be used in their original coordinates. This is crucial to increase the sensitivity of the whole pipeline without any loss in specificity.

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

Core scan image analysis corrected for cracks, ODP Hole 154-926C

Drill cores are essential for the study of deep-sea sediments and on-land sites because often no suitable outcrop is available or accessible. These cores form the backbone of stratigraphical studies using and combining various dating techniques. Cyclostratigraphy is usually based on fast and inexpensive measurements of physical sediment properties. One indirect but highly valuable proxy for reconstructing the sediment composition and variability is sediment color. However, cracks and other disturbances in sediment cores may dramatically influence the quality of color data retrieved either directly from photospectrometry or derived from core image analysis. Here we present simple but powerful algorithms to extract color data from core images, and focus on routines to exclude cracks from these images. Results are discussed using the example of an ODP core from the Ceara Rise in the Central Atlantic. The crack correction approach presented highly improves the quality of color data and allows the easy incorporation of cracked cores into studies based on core images. This facilitates the quick and inexpensive generation of large color datasets directly from quantified core images, for cyclostratigraphy and other purposes.

Steregraph image of University Hall, University Museum, and State Street, ca. 1880

2 scans. 1of2 = entire card, 2of2 = images alone, with color correction

Determination of a misfit dislocation complex in SiGe/Si heterostructures by image deconvolution technique in HREM

The core structure of a dislocation complex in SiGe/Si system composed of a perfect 60degrees dislocation and an extended 60 dislocation has been revealed at atomic level. This is attained by applying the image deconvolution technique in combination with dynamical diffraction effect correction to an image taken with a 200 kV field-emission high-resolution electron microscope. The possible configuration of the dislocation complex is analyzed and their Burgers vectors are determined. (C) 2003 Elsevier B.V. All rights reserved.

A novel phantom and method for comprehensive 3-dimensional measurement and correction of geometric distortion in magnetic resonance imaging

A phantom that can be used for mapping geometric distortion in magnetic resonance imaging (MRI) is described. This phantom provides an array of densely distributed control points in three-dimensional (3D) space. These points form the basis of a comprehensive measurement method to correct for geometric distortion in MR images arising principally from gradient field non-linearity and magnet field inhomogeneity. The phantom was designed based on the concept that a point in space can be defined using three orthogonal planes. This novel design approach allows for as many control points as desired. Employing this novel design, a highly accurate method has been developed that enables the positions of the control points to be measured to sub-voxel accuracy. The phantom described in this paper was constructed to fit into a body coil of a MRI scanner, (external dimensions of the phantom were: 310 mm x 310 mm x 310 mm), and it contained 10,830 control points. With this phantom, the mean errors in the measured coordinates of the control points were on the order of 0.1 mm or less, which were less than one tenth of the voxel's dimensions of the phantom image. The calculated three-dimensional distortion map, i.e., the differences between the image positions and true positions of the control points, can then be used to compensate for geometric distortion for a full image restoration. It is anticipated that this novel method will have an impact on the applicability of MRI in both clinical and research settings. especially in areas where geometric accuracy is highly required, such as in MR neuro-imaging. (C) 2004 Elsevier Inc. All rights reserved.

Face recognition robust to head pose from one sample image

Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly controlled for good recognition performance. In 2004, we proposed a new face recognition algorithm, Adaptive Principal Component Analysis (APCA) [4], which performs well against both lighting variation and expression change. But like other eigenface-derived face recognition algorithms, APCA only performs well with frontal face images. The work presented in this paper is an extension of our previous work to also accommodate variations in head pose. Following the approach of Cootes et al, we develop a face model and a rotation model which can be used to interpret facial features and synthesize realistic frontal face images when given a single novel face image. We use a Viola-Jones based face detector to detect the face in real-time and thus solve the initialization problem for our Active Appearance Model search. Experiments show that our approach can achieve good recognition rates on face images across a wide range of head poses. Indeed recognition rates are improved by up to a factor of 5 compared to standard PCA.

Dynamic breast MRI: Image registration and its impact on enhancement curve estimation

A novel algorithm for performing registration of dynamic contrast-enhanced (DCE) MRI data of the breast is presented. It is based on an algorithm known as iterated dynamic programming originally devised to solve the stereo matching problem. Using artificially distorted DCE-MRI breast images it is shown that the proposed algorithm is able to correct for movement and distortions over a larger range than is likely to occur during routine clinical examination. In addition, using a clinical DCE-MRI data set with an expertly labeled suspicious region, it is shown that the proposed algorithm significantly reduces the variability of the enhancement curves at the pixel level yielding more pronounced uptake and washout phases.

The geometric correction and registration of airborne line-scanned imagery for temporal thermal studies

This thesis begins by providing a review of techniques for interpreting the thermal response at the earth's surface acquired using remote sensing technology. Historic limitations in the precision with which imagery acquired from airborne platforms can be geometrically corrected and co-registered has meant that relatively little work has been carried out examining the diurnal variation of surface temperature over wide regions. Although emerging remote sensing systems provide the potential to register temporal image data within satisfactory levels of accuracy, this technology is still not widely available and does not address the issue of historic data sets which cannot be rectified using conventional parametric approaches. In overcoming these problems, the second part of this thesis describes the development of an alternative approach for rectifying airborne line-scanned imagery. The underlying assumption that scan lines within the imagery are straight greatly reduces the number of ground control points required to describe the image geometry. Furthermore, the use of pattern matching procedures to identify geometric disparities between raw line-scanned imagery and corresponding aerial photography enables the correction procedure to be almost fully automated. By reconstructing the raw image data on a truly line-by-line basis, it is possible to register the airborne line-scanned imagery to the aerial photography with an average accuracy of better than one pixel. Providing corresponding aerial photography is available, this approach can be applied in the absence of platform altitude information allowing multi-temporal data sets to be corrected and registered.

Accommodation in young adults wearing multifocal soft contact lenses under long- and short- wavelength lighting

Purpose: Several studies have suggested accommodative lags may serve as a stimulus for myopic growth, and while a blurred foveal image is believed to the main stimulus for accommodation, spectral composition of the retinal image is also believed to influence accommodative accuracy. Of particular interest is how altering spectral lighting conditions influences accommodation in the presence of soft multifocal contact lenses, which are currently being used off-label for myopia control. Methods: Accommodative responses were assessed using a Grand Seiko WAM-5500 autorefractor for four target distances: 25, 33, 50, and 100cm for 30 young adult subjects (14 myopic, 16 emmetropic; mean refractive errors (±SD, D) -4.22±2.04 and -0.15±0.67 respectively). Measurements were obtained with four different soft contact lenses, Single vision distance (SVD), Single vision near (SVN), Centre-Near (CN) and Centre-Distance (CD) (+1.50 add), and three different lighting conditions: red (peak λ 632nm), blue (peak λ 460nm), and white (peak λ 560nm). Corrections for chromatic differences in refraction were made prior to calculating accommodative errors. Results: The size of accommodative errors was significantly affected by lens design (p<0.001), lighting (p=0.027), and target distance (p=0.009). Mean accommodative errors were significantly larger with the SV lenses compared to the CD and CN designs (p<0.001). Errors were also significantly larger under blue light compared to white (p=0.004) and a significant interaction noted between lens design and lighting (p<0.001). Blue light generally decreased accommodative lags and increased accommodative leads relative to white and red light, the opposite was true of red light (p≤0.001). Lens design also significantly influenced direction of accommodative error (i.e. lag or lead) (p<0.001). Interactions with or between refractive groups were not found to be statistically significant for either the magnitude or direction of accommodative error (p>0.05 for all). Conclusions: Accuracy of accommodation is affected by both lens design and by wavelength of lighting. These accommodative lag data lend some support to recent speculation about the potential therapeutic value of lighting with a spectral bias towards blue during near work for myopia, although such treatment effects are likely to be more subtle under broad compared to the narrow spectrum lighting conditions used here.

Motion correction algorithm of lung tumors for respiratory gated PET images

Respiratory gating in lung PET imaging to compensate for respiratory motion artifacts is a current research issue with broad potential impact on quantitation, diagnosis and clinical management of lung tumors. However, PET images collected at discrete bins can be significantly affected by noise as there are lower activity counts in each gated bin unless the total PET acquisition time is prolonged, so that gating methods should be combined with imaging-based motion correction and registration methods. The aim of this study was to develop and validate a fast and practical solution to the problem of respiratory motion for the detection and accurate quantitation of lung tumors in PET images. This included: (1) developing a computer-assisted algorithm for PET/CT images that automatically segments lung regions in CT images, identifies and localizes lung tumors of PET images; (2) developing and comparing different registration algorithms which processes all the information within the entire respiratory cycle and integrate all the tumor in different gated bins into a single reference bin. Four registration/integration algorithms: Centroid Based, Intensity Based, Rigid Body and Optical Flow registration were compared as well as two registration schemes: Direct Scheme and Successive Scheme. Validation was demonstrated by conducting experiments with the computerized 4D NCAT phantom and with a dynamic lung-chest phantom imaged using a GE PET/CT System. Iterations were conducted on different size simulated tumors and different noise levels. Static tumors without respiratory motion were used as gold standard; quantitative results were compared with respect to tumor activity concentration, cross-correlation coefficient, relative noise level and computation time. Comparing the results of the tumors before and after correction, the tumor activity values and tumor volumes were closer to the static tumors (gold standard). Higher correlation values and lower noise were also achieved after applying the correction algorithms. With this method the compromise between short PET scan time and reduced image noise can be achieved, while quantification and clinical analysis become fast and precise.

Motion Correction Algorithm of Lung Tumors for Respiratory Gated PET Images

Respiratory gating in lung PET imaging to compensate for respiratory motion artifacts is a current research issue with broad potential impact on quantitation, diagnosis and clinical management of lung tumors. However, PET images collected at discrete bins can be significantly affected by noise as there are lower activity counts in each gated bin unless the total PET acquisition time is prolonged, so that gating methods should be combined with imaging-based motion correction and registration methods. The aim of this study was to develop and validate a fast and practical solution to the problem of respiratory motion for the detection and accurate quantitation of lung tumors in PET images. This included: (1) developing a computer-assisted algorithm for PET/CT images that automatically segments lung regions in CT images, identifies and localizes lung tumors of PET images; (2) developing and comparing different registration algorithms which processes all the information within the entire respiratory cycle and integrate all the tumor in different gated bins into a single reference bin. Four registration/integration algorithms: Centroid Based, Intensity Based, Rigid Body and Optical Flow registration were compared as well as two registration schemes: Direct Scheme and Successive Scheme. Validation was demonstrated by conducting experiments with the computerized 4D NCAT phantom and with a dynamic lung-chest phantom imaged using a GE PET/CT System. Iterations were conducted on different size simulated tumors and different noise levels. Static tumors without respiratory motion were used as gold standard; quantitative results were compared with respect to tumor activity concentration, cross-correlation coefficient, relative noise level and computation time. Comparing the results of the tumors before and after correction, the tumor activity values and tumor volumes were closer to the static tumors (gold standard). Higher correlation values and lower noise were also achieved after applying the correction algorithms. With this method the compromise between short PET scan time and reduced image noise can be achieved, while quantification and clinical analysis become fast and precise.

A stereo-image analysis JAVA software with links to executable and source code

Scientists planning to use underwater stereoscopic image technologies are often faced with numerous problems during the methodological implementations: commercial equipment is too expensive; the setup or calibration is too complex; or the imaging processing (i.e. measuring objects in the stereo-images) is too complicated to be performed without a time-consuming phase of training and evaluation. The present paper addresses some of these problems and describes a workflow for stereoscopic measurements for marine biologists. It also provides instructions on how to assemble an underwater stereo-photographic system with two digital consumer cameras and gives step-by-step guidelines for setting up the hardware. The second part details a software procedure to correct stereo-image pairs for lens distortions, which is especially important when using cameras with non-calibrated optical units. The final part presents a guide to the process of measuring the lengths (or distances) of objects in stereoscopic image pairs. To reveal the applicability and the restrictions of the described systems and to test the effects of different types of camera (a compact camera and an SLR type), experiments were performed to determine the precision and accuracy of two generic stereo-imaging units: a diver-operated system based on two Olympus Mju 1030SW compact cameras and a cable-connected observatory system based on two Canon 1100D SLR cameras. In the simplest setup without any correction for lens distortion, the low-budget Olympus Mju 1030SW system achieved mean accuracy errors (percentage deviation of a measurement from the object's real size) between 10.2 and -7.6% (overall mean value: -0.6%), depending on the size, orientation and distance of the measured object from the camera. With the single lens reflex (SLR) system, very similar values between 10.1% and -3.4% (overall mean value: -1.2%) were observed. Correction of the lens distortion significantly improved the mean accuracy errors of either system. Even more, system precision (spread of the accuracy) improved significantly in both systems. Neither the use of a wide-angle converter nor multiple reassembly of the system had a significant negative effect on the results. The study shows that underwater stereophotography, independent of the system, has a high potential for robust and non-destructive in situ sampling and can be used without prior specialist training.

Mosaic image of the artificial light produced by Berlin on the night of September 11, 2010 observed from an altitude of 10,000 ft

Aerial observations of light pollution can fill an important gap between ground based surveys and nighttime satellite data. Terrestrially bound surveys are labor intensive and are generally limited to a small spatial extent, and while existing satellite data cover the whole world, they are limited to coarse resolution. This paper describes the production of a high resolution (1 m) mosaic image of the city of Berlin, Germany at night. The dataset is spatially analyzed to identify themajor sources of light pollution in the city based on urban land use data. An area-independent 'brightness factor' is introduced that allows direct comparison of the light emission from differently sized land use classes, and the percentage area with values above average brightness is calculated for each class. Using this methodology, lighting associated with streets has been found to be the dominant source of zenith directed light pollution (31.6%), although other land use classes have much higher average brightness. These results are compared with other urban light pollution quantification studies. The minimum resolution required for an analysis of this type is found to be near 10 m. Future applications of high resolution datasets such as this one could include: studies of the efficacy of light pollution mitigation measures, improved light pollution simulations, economic and energy use, the relationship between artificial light and ecological parameters (e.g. circadian rhythm, fitness, mate selection, species distributions, migration barriers and seasonal behavior), or the management of nightscapes. To encourage further scientific inquiry, the mosaic data is freely available at Pangaea.