Temporal Sequence Of Images Research Articles

Laboratory Measurement of the DInSAR Response to Spatiotemporal Variations in Soil Moisture

Differential interferometric synthetic aperture radar (DInSAR) has traditionally been used for the detection and accurate monitoring of surface movement in a scene and has found applications in fields such as mining subsidence and earthquake deformation. In these studies, the phase is understood to directly relate to the radial component of the physical deformation of the surface. In this paper, however, we use a novel combination of microwave and optical laboratory measurements to demonstrate the presence of persistent and coherent phase changes in a temporal sequence of DInSAR images, related solely to moisture change in a soil. This is confirmation of recent reports suggesting that, in some circumstances, the DInSAR signal may include a significant soil moisture signal. Laboratory measurements were used to obtain a set of high-resolution C-band DInSAR images of a sandy soil sample of an area of 2.0 m × 1.8 m and a depth of 0.2 m, with the fractional volumetric water content varying between 0.1 and 0.4. To independently monitor the soil surface for physical movement, a time-lapsed set of high-resolution digital optical images was continuously acquired. Although the soil underwent a large moisture change, the soil surface was static to within ±0.1 mm over the majority of the experiment. The DInSAR sequence displayed dynamic and complex variations of the phase, although a linear relationship with moisture change was evident when the mean phase change was considered. The work raises the possibility that DInSAR could be used for the monitoring of soil moisture change in a scene, a parameter of significant economic and environmental importance.

Performance of optical flow techniques for motion analysis of fluorescent point signals in confocal microscopy

Optical flow approaches calculate vector fields which determine the apparent velocities of objects in time-varying image sequences. They have been analyzed extensively in computer science using both natural and synthetic video sequences. In life sciences, there is an increasing need to extract kinetic information from temporal image sequences which reveals the interplay between form and function of microscopic biological structures. In this work, we test different variational optical flow techniques to quantify the displacements of biological objects in 2D fluorescent image sequences. The accuracy of the vector fields is tested for defined displacements of fluorescent point sources in synthetic image series which mimic protein traffic in neuronal dendrites, and for GABABR1 receptor subunits in dendrites of hippocampal neurons. Our results reveal that optical flow fields predict the movement of fluorescent point sources within an error of 3% for a maximum displacement of 160 nm. Displacement of agglomerated GABABR1 receptor subunits can be predicted correctly for maximum displacements of 640 nm. Based on these results, we introduce a criteria to derive the optimum parameter combinations for the calculation of the optical flow fields in experimental images. From these results, temporal sampling frequencies for image acquisition can be derived to guarantee correct motion estimation for biological objects.

Motion Complexity Analysis with Automated Lung Field Tracking in MRI Temporal Sequences

AbstractWe propose a new method for evaluating the lung field motion complexity in magnetic resonance (MR) image temporal sequences, where complexity is defined as the minimum number of respiratory movement patterns to represent the detected lung field motion. In the first part of this work, a method for high precision landmark determination and tracking on a temporal sequences of chest magnetic resonance images is proposed. The landmark determination and tracking algorithms are based on a hierarchical template matching algorithm. A stable matching is made with some additional rules to avoid mistracking. In the second part of this work, the optimum respiratory movement patterns are determined by minimizing the mean square error applied to the determined paths. The proposed algorithm was tested with 29 subjects, including healthy subjects and COPD (Chronic Obstructive Pulmonary Disease) patients. The paths obtained by the landmark tracking algorithm represent the movement of some parenchyma internal structures. The paths automatically obtained were analyzed and compared to paths manually obtained by a medical specialist. It is concluded that the respiratory movement patterns necessary to represent the paths determined manually and automatically for the same subjects were compatible. The results also show that COPD patients have two main respiratory movement patterns. Copyright©2011 IFAC.

Registration of temporal sequences of coronal and sagittal MR images through respiratory patterns

This work discusses the determination of the breathing patterns in time sequence of images obtained from magnetic resonance (MR) and their use in the temporal registration of coronal and sagittal images. The registration is made without the use of any triggering information and any special gas to enhance the contrast. The temporal sequences of images are acquired in free breathing. The real movement of the lung has never been seen directly, as it is totally dependent on its surrounding muscles and collapses without them. The visualization of the lung in motion is an actual topic of research in medicine. The lung movement is not periodic and it is susceptible to variations in the degree of respiration. Compared to computerized tomography (CT), MR imaging involves longer acquisition times and it is preferable because it does not involve radiation. As coronal and sagittal sequences of images are orthogonal to each other, their intersection corresponds to a segment in the three-dimensional space. The registration is based on the analysis of this intersection segment. A time sequence of this intersection segment can be stacked, defining a two-dimension spatio-temporal (2DST) image. The algorithm proposed in this work can detect asynchronous movements of the internal lung structures and lung surrounding organs. It is assumed that the diaphragmatic movement is the principal movement and all the lung structures move almost synchronously. The synchronization is performed through a pattern named respiratory function. This pattern is obtained by processing a 2DST image. An interval Hough transform algorithm searches for synchronized movements with the respiratory function. A greedy active contour algorithm adjusts small discrepancies originated by asynchronous movements in the respiratory patterns. The output is a set of respiratory patterns. Finally, the composition of coronal and sagittal image pairs that are in the same breathing phase is realized by comparing of respiratory patterns originated from diaphragmatic and upper boundary surfaces. When available, the respiratory patterns associated to lung internal structures are also used. The results of the proposed method are compared with the pixel-by-pixel comparison method. The proposed method increases the number of registered pairs representing composed images and allows an easy check of the breathing phase.

Multilevel space-time aggregation for bright field cell microscopy segmentation and tracking.

A multilevel aggregation method is applied to the problem of segmenting live cell bright field microscope images. The method employed is a variant of the so-called “Segmentation by Weighted Aggregation” technique, which itself is based on Algebraic Multigrid methods. The variant of the method used is described in detail, and it is explained how it is tailored to the application at hand. In particular, a new scale-invariant “saliency measure” is proposed for deciding when aggregates of pixels constitute salient segments that should not be grouped further. It is shown how segmentation based on multilevel intensity similarity alone does not lead to satisfactory results for bright field cells. However, the addition of multilevel intensity variance (as a measure of texture) to the feature vector of each aggregate leads to correct cell segmentation. Preliminary results are presented for applying the multilevel aggregation algorithm in space time to temporal sequences of microscope images, with the goal of obtaining space-time segments (“object tunnels”) that track individual cells. The advantages and drawbacks of the space-time aggregation approach for segmentation and tracking of live cells in sequences of bright field microscope images are presented, along with a discussion on how this approach may be used in the future work as a building block in a complete and robust segmentation and tracking system.

Detecting molecular separation in nano-fluidic channels through velocity analysis of temporal image sequences by multivariate curve resolution

In this study, we report on a method of determining individual velocities of molecular species being separated in a fluid medium within array of nanofluidic channels that can be useful in the detection of molecular species. The method is based on the application of multivariate image analysis methods, in this case principal component analysis and multivariate curve resolution, to temporal image series capturing multiple species moving through the medium. There are two novel and unique advantages of the reported method. First, it is possible to identify transport velocities of different molecular species, even those tagged with the same fluorophore. And second, the velocity determination can be made before there is any visual separation of the species in the medium at the very initial stages of separation. The capability of the methodology to detect the separation of species without fluorescent labeling and to provide an accurate ratio of their velocities even at the very early pre-visual stage of separation will significantly optimize separation experiments and assist in fast and accurate detection of analytes based on micro- and nano-fluidics assays. The presented method can be practiced in connection with various molecular separation techniques including, but not limited to, nanochannel electrophoresis, microchannel capillary electrophoresis, and gel electrophoresis.

Imagens de sensoriamento remoto no monitoramento da colheita da cana-de-açúcar

A prática agrícola da queima da palha da cana-de-açúcar tem por finalidade facilitar a colheita manual. No Estado de São Paulo, essa prática está submetida a uma rigorosa legislação ambiental devido ao impacto negativo para o meio ambiente. Está previsto que a queima da cana deve ser gradativamente eliminada até 2017. O presente trabalho tem por objetivo avaliar a área de cana colhida com e sem queima em todo o Estado de São Paulo. Para tal, foram utilizadas as imagens disponíveis do sensor TM a bordo do satélite Landsat-5. As imagens foram adquiridas de abril a dezembro de 2006, que corresponde ao período de colheita da cana. Essas imagens foram analisadas por meio de técnicas de processamento digital e interpretação visual. A área de cana colhida sem queima foi avaliada em 1.085.730 ha e corresponde a 34,7% do total da área mecanizável colhida. Isso atende à legislação ambiental que, para o ano de 2006, previa que 30% da área de cana fosse colhida sem queima. A sequência temporal de imagens adquiridas entre abril e dezembro permite identificar as áreas de cana colhidas sem queima e, portanto, distingui-las das áreas de cana colhidas com queima.

Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal Means

This paper presents a new algorithm for denoising dynamic contrast-enhanced (DCE) MR images. It is a novel variation on the nonlocal means (NLM) algorithm. The algorithm, called dynamic nonlocal means (DNLM), exploits the redundancy of information in the temporal sequence of images. Empirical evaluations of the performance of the DNLM algorithm relative to seven other denoising methods-simple Gaussian filtering, the original NLM algorithm, a trivial extension of NLM to include the temporal dimension, bilateral filtering, anisotropic diffusion filtering, wavelet adaptive multiscale products threshold, and traditional wavelet thresholding-are presented. The evaluations include quantitative evaluations using simulated data and real data (20 DCE-MRI data sets from routine clinical breast MRI examinations) as well as qualitative evaluations using the same real data (24 observers: 14 image/signal-processing specialists, 10 clinical breast MRI radiographers). The results of the quantitative evaluation using the simulated data show that the DNLM algorithm consistently yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the quantitative evaluation using the real data provide evidence, at the alpha = 0.05 level of significance, that the DNLM algorithm yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the qualitative evaluation provide evidence, at the alpha = 0.05 level of significance, that the DNLM algorithm performs visually better than all of the other algorithms. Collectively the qualitative and quantitative results suggest that the DNLM algorithm more effectively attenuates noise in DCE MR images than any of the other algorithms.

Lung Movement Determination in Temporal Sequences of MR Images Using Hough Transform and Interval Arithmetics

Since the lung is an organ whose real movement has never been seen, as it is totally dependent on the rib cage and collapses without them, the objective of the present work is to develop a method to study its movement. The visualization of the lung in motion is an actual topic of research in medicine. Computerized Tomography (CT) can obtain spatio–temporal images of the heart by synchronizing with electrocardiographic waves. The field of view (FOV) of the heart is small when compared to the lung's FOV. The lung's movement is not periodic and it is susceptible to variations in the degree of respiration. Compared to CT, Magnetic Resonance (MR) imaging involves longer acquisition times and it is preferable because they do not involve radiation. The breathing is associated to a standard respiratory function, and through 2D image processing, edge detection, Hough transform and interval arithmetics, respiratory functions are obtained and, consequently, the position of the points in time are estimated. A standard respiratory function must be provided as input to the modified Hough transform. The Hough transform uses a discrete quantization space, implemented as an accumulator matrix. An improved Hough transform is proposed by using interval arithmetics in the accumulation phase of the algorithm. The discreteness of the quantization space is considered in the algorithm, and its consequences are minimized. This work showed more consistent results when compared to previous approaches. Copyright ©2009 IFAC.

Low-Noise Dynamic Reconstruction for X-Ray Tomographic Perfusion Studies Using Low Sampling Rates

Functional imaging based on tomographic X-ray imaging relies on the reconstruction of a temporal sequence of images which accurately reproduces the time attenuation curves of the tissue. The main constraints of these techniques are temporal resolution and dose. Using current techniques the data acquisition has to be performed fast so that the dynamic attenuation values can be regarded as static during the scan. Due to the relatively high number of repeated scans the dose per single scan has to be low yielding a poor signal-to-noise ratio (SNR) in the reconstructed images. In a previous publication a temporal interpolation scheme in the projection data space was relaxing the temporal resolution constraint. The aim of this contribution is the improvement of the SNR. A temporal smoothing term is introduced in the temporal interpolation scheme such that only the physiologic relevant bandwidth is considered. A significant increase of the SNR is achieved. The obtained level of noise only depends on the total dose applied and is independent of the number of scans and the SNR of a single reconstructed image. The approach might be the first step towards using slowly rotating CT systems for perfusion imaging like C-arm or small animal CT scanners.

A non-rigid motion estimation algorithm for yawn detection in human drivers

This work focuses on the estimation of possible fatigue or drowsiness by detecting the occurrence of yawns with human drivers. An image processing technique has been proposed to analyse the deformation occurring on driver's face and accurately identify the yawn from other types of mouth opening such as talking and singing. The algorithm quantifies the degree of deformation on lips when a driver yawns. The image processing methodology is based on study of non-rigid motion patterns on 2D images. The analysis is done on a temporal sequence of images acquired by a camera. A shape-based correspondence of templates on contours of a particular region is established on the basis of curvature information. The shape similarity between the contours is analysed, after decomposing with wavelets at different levels. Finally, the yawn is correlated with fatigue-induced behaviour of drivers on a simulator.

Motion Analysis of the Carotid Artery Wall and Plaque Using B-Mode Ultrasound

Abstract: Motion of the arterial wall and atheromatous plaque, especially in the longitudinal direction, has recently gained attention as a determinant of carotid atherosclerosis. Vessel wall motion, caused by blood pressure, blood flow and tethering to the surrounding tissue, may be responsible for tissue rupture and cerebrovascular symptoms. B-mode ultrasound allows non-invasive recording of arterial wall and plaque motion in two directions, namely radial and longitudinal. Temporal sequences of ultrasound images, recorded at high frame rates, can be used to quantitatively estimate movement of the arterial wall. Motion of the carotid artery wall and plaque from sequences of ultrasound images has been estimated using block matching and optical flow techniques. Block matching is based on tracking blocks of pixels, assuming that the blocks remain constant over time and motion. Optical flow relies on the estimation of the spatiotemporal change of individual pixel intensities throughout a sequence, resulting in a dense vector map where each pixel is represented by a vector corresponding to its velocity between two frames. An important issue in studies of arterial wall motion is the validation of the motion analysis techniques. Tissue-mimicking phantoms as well as computer-generated simulation images have been used to validate motion analysis algorithms. In conclusion, motion of the carotid artery wall and plaque can be quantitatively estimated from B-mode ultrasound and may represent a powerful tool to study further the mechanisms of atherosclerosis.

Estimation of insect infestation dynamics using a temporal sequence of Landsat data

The current outbreak of mountain pine beetle ( Dendroctonus ponderosae) in western Canada has been increasing over the past decade and is currently estimated to be impacting 9.2 million hectares, with varying levels of severity. Large area insect monitoring is typically undertaken using manual aerial overview sketch mapping, whereby an interpreter depicts areas of homogenous insect attack conditions onto 1:250,000 or 1:100,000 scale maps. These surveys provide valuable strategic data for management at the provincial scale. The coarse spatial and attribute resolution of these data however, make them inappropriate for fine-scale monitoring and operational planning. For instance, it is not possible to estimate the initial timing of attack and year of stand death. In this study, we utilise eight Landsat scenes collected over a 14 year period in north-central British Columbia, Canada, where the infestation has gradually developed both spatially and temporally. After pre-processing and normalising the eight scenes using a relative normalisation procedure, decision tree analysis was applied to classify spectral trajectories of the Normalised Difference Moisture Index (NDMI). From the classified temporal sequence of images, key parameters were extracted including the presence of beetle disturbance and timing of stand decline. The accuracy of discriminating beetle attack from healthy forest stands was assessed both spatially and temporally using three years of aerial survey data (1996, 2003, and 2004) with results indicating overall classification accuracies varying between 71 and 86%. As expected, the earliest and least severe attack year (1996), recorded the lowest overall accuracy. The relationship between the timing of stand attack ( i.e. moderate to severe beetle infestation) and NDMI (initial year of detected disturbance) was also explored. The results suggest that there is potential for deriving regional estimates of the year of stand death using Landsat data and decision tree analysis however, a higher temporal frequency of images is required to quantify the timing of mountain pine beetle attack.

Face Recognition Based on Mutual Projection of Feature Distributions

This paper proposes a new face recognition method based on mutual projection of feature distributions. The proposed method introduces a new robust measurement between two feature distributions. This measurement is computed by a harmonic mean of two distance values obtained by projection of each mean value into the opposite feature distribution. The proposed method does not require eigenvalue analysis of the two subspaces. This method was applied to face recognition task of temporal image sequence. Experimental results demonstrate that the computational cost was improved without degradation of identification performance in comparison with the conventional method.

Definition and recovery of kinematic features for recognition of American sign language movements

An approach to recognizing human hand gestures from a monocular temporal sequence of images is presented. Of concern is the representation and recognition of hand movements that are used in single-handed American sign language (ASL). The approach exploits previous linguistic analysis of manual languages that decompose dynamic gestures into their static and dynamic components. The first level of decomposition is in terms of three sets of primitives, hand shape, location and movement. Further levels of decomposition involve the lexical and sentence levels and are beyond the scope of the present paper. We propose and subsequently demonstrate that given a monocular gesture sequence, kinematic features can be recovered from the apparent motion that provide distinctive signatures for 14 primitive movements of ASL. The approach has been implemented in software and evaluated on a database of 592 gesture sequences with an overall recognition rate of 86% for fully automated processing and 97% for manually initialized processing.

Use of the ASTER satellite images for evaluation of structural changes in the Popocatépetl volcano related to microseismicity

Mexico is one of the most volcanically active regions in North America. Volcanic activity in central Mexico is associated with the subduction of the Cocos and Rivera plates beneath the North American plate. Periods of enhanced microseismic activity, associated with the volcanic activity of the Popocatépetl volcano are compared with periods, during which the microseismic activity was low. We detected systematical changes in the number of lineaments, associated with the microseismic activity due to lineament analysis of a temporal sequence of high resolution satellite images of the Popocatépetl volcano, provided by the ASTER/VNIR instrument. The Lineament Extraction and Stripes Statistic Analysis (LESSA) software package was used for the lineament extraction. In the future it would allow develop a methodology for detection of possible elevation of pressure in volcano edifice.

Analyzing NEXRAD doppler radar images to assess nightly dispersal patterns and population trends in Brazilian free-tailed bats (Tadarida brasiliensis)

Operators of early weather-surveillance radars often observed echoes on their displays that did not behave like weather pattern, including expanding ring-like shapes they called angels. These echoes were caused by high-flying insects, migrating birds, and large colonies of bats emerging from roosts to feed. Modern weather-surveillance radar stations in the United States (NEXt-generation RADar or NEXRAD) provide detailed images that clearly show evening bat emergences from large colonies. These images can be used to investigate the flight behavior of groups of bats and population trends in large colonies of Brazilian free-tailed bats (Tadarida brasiliensis) in south-central Texas which are clearly imaged by local NEXRAD radar stations. In this study, we used radar reflectivity data from the New Braunfels, Texas NEXRAD station to examine relative colony size, direction of movement, speed of dispersion, and altitude gradients of bats from these colonies following evening emergence. Base reflectivity clear-air-mode Level-II images were geo-referenced and compiled in a GIS along with locations of colonies and features on the landscape. Temporal sequences of images were filtered for the activity of bats, and from this, the relative size of bat colonies, and the speed and heading of bat emergences were calculated. Our results indicate cyclical changes in colony size from year to year and that initial headings taken by bats during emergence flights are highly directional. We found that NEXRAD data can be an effective tool for monitoring the nightly behavior and seasonal changes in these large colonies. Understanding the distribution of a large regional bat population on a landscape scale has important implications for agricultural pest management and conservation efforts.

Monitoring redox-sensitive paramagnetic contrast agent by EPRI, OMRI and MRI

A comparative study of tissue redox-status imaging using commonly used redox sensitive nitroxides has been carried out using electron paramagnetic resonance imaging (EPRI), Overhauser magnetic resonance imaging (OMRI) and conventional T 1-weighted magnetic resonance imaging, MRI. Imaging studies using phantoms of different nitroxides at different concentration levels showed that EPRI and OMRI sensitivities were found to be linearly dependent on line width of nitroxides up to 2 mM, and the enhancement in MRI intensity was linear up to 5 mM. The sensitivity and resolution of EPRI and OMRI images depended significantly on the line width of the nitroxides whereas the MRI images were almost independent of EPR line width. Reduction of the paramagnetic 3-carbamoyl-2,2,5,5-tetramethylpyrrolidine-1-oxyl (3CP) by ascorbic acid (AsA) to the diamagnetic by hydroxylamine was monitored from a sequence of temporal images, acquired using the three imaging modalities. The decay rates determined by all the three modalities were found to be similar. However the results suggest that T 1-weighted MRI can monitor the redox status, in addition to providing detailed anatomical structure in a short time. Therefore, a combination of MRI with nitroxides as metabolically responsive contrast agents can be a useful technique for the in vivo imaging probing tissue redox status.

Animated solid model of the lung constructed from unsynchronized MR sequential images

This work discusses a 4D lung reconstruction method from unsynchronized MR sequential images. The lung, differently from the heart, does not have its own muscles, turning impossible to see its real movements. The visualization of the lung in motion is an actual topic of research in medicine. CT (Computerized Tomography) can obtain spatio-temporal images of the heart by synchronizing with electrocardiographic waves. The FOV of the heart is small when compared to the lung’s FOV. The lung’s movement is not periodic and is susceptible to variations in the degree of respiration. Compared to CT, MR (Magnetic Resonance) imaging involves longer acquisition times and it is not possible to obtain instantaneous 3D images of the lung. For each slice, only one temporal sequence of 2D images can be obtained. However, methods using MR are preferable because they do not involve radiation. In this paper, based on unsynchronized MR images of the lung an animated B-Rep solid model of the lung is created. The 3D animation represents the lung’s motion associated to one selected sequence of MR images. The proposed method can be divided in two parts. First, the lung’s silhouettes moving in time are extracted by detecting the presence of a respiratory pattern on 2D spatio-temporal MR images. This approach enables us to determine the lung’s silhouette for every frame, even on frames with obscure edges. The sequence of extracted lung’s silhouettes are unsynchronized sagittal and coronal silhouettes. Using our algorithm it is possible to reconstruct a 3D lung starting from a silhouette of any type (coronal or sagittal) selected from any instant in time. A wire–frame model of the lung is created by composing coronal and sagittal planar silhouettes representing cross-sections. The silhouette composition is severely underconstrained. Many wire–frame models can be created from the observed sequences of silhouettes in time. Finally, a B-Rep solid model is created using a meshing algorithm. Using the B-Rep solid model the volume in time for the right and left lungs were calculated. It was possible to recognize several characteristics of the 3D real right and left lungs in the shaded model.

The Pixelised Wavelet Filtering Method to Study Waves and Oscillations in Time Sequences of Solar Atmospheric Images

Pixelised wavelet filtering (PWF) for the determination of the spatial, temporal, and phase structure of oscillation sources in temporal sequences of 2D images, based upon the continuous wavelet transform, has been designed and tested. The PWF method allows us to obtain information about the presence of propagating and nonpropagating waves in the data and localise them precisely in time and in space. The method is tested on the data sets obtained in microwaves with the Nobeyama Radioheliograph and in the EUV with TRACE. The method reveals fine spatial structuring of the sources of 3-, 5-, and 15-minute periodicities in the microwave and EUV emission generated in sunspot atmospheres. In addition, the PWF method provides us with unique information about the temporal variability of the power, amplitude, and phase narrowband maps of the observed oscillations and waves. The applicability of the method to the analysis of coronal wave phenomena is discussed.