A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Simultaneous cell morphometry and refractive index measurement with dual-wavelength digital holographic microscopy and dye-enhanced dispersion of perfusion medium.

Digital holographic microscopy (DHM) allows optical-path-difference (OPD) measurements with nanometric accuracy. OPD induced by transparent cells depends on both the refractive index (RI) of cells and their morphology. This Letter presents a dual-wavelength DHM that allows us to separately measure both the RI and the cellular thickness by exploiting an enhanced dispersion of the perfusion medium achieved by the utilization of an extracellular dye. The two wavelengths are chosen in the vicinity of the absorption peak of the dye, where the absorption is accompanied by a significant variation of the RI as a function of the wavelength.

Structured output SVM for remote sensing image classification

In the recent years, kernel methods have revealed very powerful tools in many application domains in general and in remote sensing image classification in particular. The special characteristics of remote sensing images (high dimension, few labeled samples and different noise sources) are efficiently dealt with kernel machines. In this paper, we propose the use of structured output learning to improve remote sensing image classification based on kernels. Structured output learning is concerned with the design of machine learning algorithms that not only implement input-output mapping, but also take into account the relations between output labels, thus generalizing unstructured kernel methods. We analyze the framework and introduce it to the remote sensing community. Output similarity is here encoded into SVM classifiers by modifying the model loss function and the kernel function either independently or jointly. Experiments on a very high resolution (VHR) image classification problem shows promising results and opens a wide field of research with structured output kernel methods.

Kinematics and dynamic complexity of postural transitions in frail elderly subjects.

The aim of this study was to propose a methodology allowing a detailed characterization of body sit-to-stand/stand-to-sit postural transition. Parameters characterizing the kinematics of the trunk movement during sit-to-stand (Si-St) postural transition were calculated using one initial sensor system fixed on the trunk and a data logger. Dynamic complexity of these postural transitions was estimated by fractal dimension of acceleration-angular velocity plot. We concluded that this method provides a simple and accurate tool for monitoring frail elderly and to objectively evaluate the efficacy of a rehabilitation program.

Comparison of three commercially available radio frequency coils for human brain imaging at 3 Tesla.

OBJECTIVE: To evaluate a transverse electromagnetic (TEM), a circularly polarized (CP) (birdcage), and a 12-channel phased array head coil at the clinical field strength of B0 = 3T in terms of signal-to-noise ratio (SNR), signal homogeneity, and maps of the effective flip angle alpha. MATERIALS AND METHODS: SNR measurements were performed on low flip angle gradient echo images. In addition, flip angle maps were generated for alpha(nominal) = 30 degrees using the double angle method. These evaluation steps were performed on phantom and human brain data acquired with each coil. Moreover, the signal intensity variation was computed for phantom data using five different regions of interest. RESULTS: In terms of SNR, the TEM coil performs slightly better than the CP coil, but is second to the smaller 12-channel coil for human data. As expected, both the TEM and the CP coils show superior image intensity homogeneity than the 12-channel coil, and achieve larger mean effective flip angles than the combination of body and 12-channel coil with reduced radio frequency power deposition. CONCLUSION: At 3T the benefits of TEM coil design over conventional lumped element(s) coil design start to emerge, though the phased array coil retains an advantage with respect to SNR performance.

A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis.

Diagnosis of several neurological disorders is based on the detection of typical pathological patterns in the electroencephalogram (EEG). This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist in quantitative analysis and interpretation. We present a method, which allows automatic detection of epileptiform events and discrimination of them from eye blinks, and is based on features derived using a novel application of independent component analysis. The algorithm was trained and cross validated using seven EEGs with epileptiform activity. For epileptiform events with compensation for eyeblinks, the sensitivity was 65 +/- 22% at a specificity of 86 +/- 7% (mean +/- SD). With feature extraction by PCA or classification of raw data, specificity reduced to 76 and 74%, respectively, for the same sensitivity. On exactly the same data, the commercially available software Reveal had a maximum sensitivity of 30% and concurrent specificity of 77%. Our algorithm performed well at detecting epileptiform events in this preliminary test and offers a flexible tool that is intended to be generalized to the simultaneous classification of many waveforms in the EEG.

Localized in vivo hyperpolarization transfer sequences.

In vivo localized and fully adiabatic homonuclear and heteronuclear polarization transfer experiments were designed and performed in the rat brain at 9.4 T after infusion of hyperpolarized sodium [1,2-(13)C(2)] and sodium [1-(13)C] acetate. The method presented herein leads to highly enhanced in vivo detection of short-T(1) (13)C as well as attached protons. This indirect detection scheme allows for probing additional molecular sites in hyperpolarized substrates and their metabolites and can thus lead to improved spectral resolution such as in the case of (13)C-acetate metabolism.

Chest pulse-wave velocity: a novel approach to assess arterial stiffness.

Pulse-wave velocity (PWV) is considered as the gold-standard method to assess arterial stiffness, an independent predictor of cardiovascular morbidity and mortality. Current available devices that measure PWV need to be operated by skilled medical staff, thus, reducing the potential use of PWV in the ambulatory setting. In this paper, we present a new technique allowing continuous, unsupervised measurements of pulse transit times (PTT) in central arteries by means of a chest sensor. This technique relies on measuring the propagation time of pressure pulses from their genesis in the left ventricle to their later arrival at the cutaneous vasculature on the sternum. Combined thoracic impedance cardiography and phonocardiography are used to detect the opening of the aortic valve, from which a pre-ejection period (PEP) value is estimated. Multichannel reflective photoplethysmography at the sternum is used to detect the distal pulse-arrival time (PAT). A PTT value is then calculated as PTT = PAT - PEP. After optimizing the parameters of the chest PTT calculation algorithm on a nine-subject cohort, a prospective validation study involving 31 normo- and hypertensive subjects was performed. 1/chest PTT correlated very well with the COMPLIOR carotid to femoral PWV (r = 0.88, p < 10 (-9)). Finally, an empirical method to map chest PTT values onto chest PWV values is explored.

Adaptive tracking of EEG oscillations.

Neuronal oscillations are an important aspect of EEG recordings. These oscillations are supposed to be involved in several cognitive mechanisms. For instance, oscillatory activity is considered a key component for the top-down control of perception. However, measuring this activity and its influence requires precise extraction of frequency components. This processing is not straightforward. Particularly, difficulties with extracting oscillations arise due to their time-varying characteristics. Moreover, when phase information is needed, it is of the utmost importance to extract narrow-band signals. This paper presents a novel method using adaptive filters for tracking and extracting these time-varying oscillations. This scheme is designed to maximize the oscillatory behavior at the output of the adaptive filter. It is then capable of tracking an oscillation and describing its temporal evolution even during low amplitude time segments. Moreover, this method can be extended in order to track several oscillations simultaneously and to use multiple signals. These two extensions are particularly relevant in the framework of EEG data processing, where oscillations are active at the same time in different frequency bands and signals are recorded with multiple sensors. The presented tracking scheme is first tested with synthetic signals in order to highlight its capabilities. Then it is applied to data recorded during a visual shape discrimination experiment for assessing its usefulness during EEG processing and in detecting functionally relevant changes. This method is an interesting additional processing step for providing alternative information compared to classical time-frequency analyses and for improving the detection and analysis of cross-frequency couplings.

SA2RAGE: a new sequence for fast B1+ -mapping.

At high magnetic field strengths (≥ 3T), the radiofrequency wavelength used in MRI is of the same order of magnitude of (or smaller than) the typical sample size, making transmit magnetic field (B1+) inhomogeneities more prominent. Methods such as radiofrequency-shimming and transmit SENSE have been proposed to mitigate these undesirable effects. A prerequisite for such approaches is an accurate and rapid characterization of the B1+ field in the organ of interest. In this work, a new phase-sensitive three-dimensional B1+-mapping technique is introduced that allows the acquisition of a 64 × 64 × 8 B1+-map in ≈ 20 s, yielding an accurate mapping of the relative B1+ with a 10-fold dynamic range (0.2-2 times the nominal B1+). Moreover, the predominant use of low flip angle excitations in the presented sequence minimizes specific absorption rate, which is an important asset for in vivo B1+-shimming procedures at high magnetic fields. The proposed methodology was validated in phantom experiments and demonstrated good results in phantom and human B1+-shimming using an 8-channel transmit-receive array.

Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy.

We have developed a digital holographic microscope (DHM), in a transmission mode, especially dedicated to the quantitative visualization of phase objects such as living cells. The method is based on an original numerical algorithm presented in detail elsewhere [Cuche et al., Appl. Opt. 38, 6994 (1999)]. DHM images of living cells in culture are shown for what is to our knowledge the first time. They represent the distribution of the optical path length over the cell, which has been measured with subwavelength accuracy. These DHM images are compared with those obtained by use of the widely used phase contrast and Nomarski differential interference contrast techniques.

Spectrally selective B1-insensitive T2 magnetization preparation sequence.

A T(2) magnetization-preparation (T(2) Prep) sequence is proposed that is insensitive to B(1) field variations and simultaneously provides fat suppression without any further increase in specific absorption rate (SAR). Increased B(1) inhomogeneity at higher magnetic field strength (B(0) > or = 3T) necessitates a preparation sequence that is less sensitive to B(1) variations. For the proposed technique, T(2) weighting in the image is achieved using a segmented B(1)-insensitive rotation (BIR-4) adiabatic pulse by inserting two equally long delays, one after the initial reverse adiabatic half passage (AHP), and the other before the final AHP segment of a BIR-4 pulse. This sequence yields T(2) weighting with both B(1) and B(0) insensitivity. To simultaneously suppress fat signal (at the cost of B(0) insensitivity), the second delay is prolonged so that fat accumulates additional phase due to its chemical shift. Numerical simulations as well as phantom and in vivo image acquisitions were performed to show the efficacy of the proposed technique.

Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches.

In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.

Towards a diffusion image processing validation and accuracy prediction framework

Validation is the main bottleneck preventing theadoption of many medical image processing algorithms inthe clinical practice. In the classical approach,a-posteriori analysis is performed based on someobjective metrics. In this work, a different approachbased on Petri Nets (PN) is proposed. The basic ideaconsists in predicting the accuracy that will result froma given processing based on the characterization of thesources of inaccuracy of the system. Here we propose aproof of concept in the scenario of a diffusion imaginganalysis pipeline. A PN is built after the detection ofthe possible sources of inaccuracy. By integrating thefirst qualitative insights based on the PN withquantitative measures, it is possible to optimize the PNitself, to predict the inaccuracy of the system in adifferent setting. Results show that the proposed modelprovides a good prediction performance and suggests theoptimal processing approach.

A new ambulatory system for comparative evaluation of the three-dimensional knee kinematics, applied to anterior cruciate ligament injuries

The aim of this study was to develop an ambulatory system for the three-dimensional (3D) knee kinematics evaluation, which can be used outside a laboratory during long-term monitoring. In order to show the efficacy of this ambulatory system, knee function was analysed using this system, after an anterior cruciate ligament (ACL) lesion, and after reconstructive surgery. The proposed system was composed of two 3D gyroscopes, fixed on the shank and on the thigh, and a portable data logger for signal recording. The measured parameters were the 3D mean range of motion (ROM) and the healthy knee was used as control. The precision of this system was first assessed using an ultrasound reference system. The repeatability was also estimated. A clinical study was then performed on five unilateral ACL-deficient men (range: 19-36 years) prior to, and a year after the surgery. The patients were evaluated with the IKDC score and the kinematics measurements were carried out on a 30 m walking trial. The precision in comparison with the reference system was 4.4 degrees , 2.7 degrees and 4.2 degrees for flexion-extension, internal-external rotation, and abduction-adduction, respectively. The repeatability of the results for the three directions was 0.8 degrees , 0.7 degrees and 1.8 degrees . The averaged ROM of the five patients' healthy knee were 70.1 degrees (standard deviation (SD) 5.8 degrees), 24.0 degrees (SD 3.0 degrees) and 12.0 degrees (SD 6.3 degrees for flexion-extension, internal-external rotation and abduction-adduction before surgery, and 76.5 degrees (SD 4.1 degrees), 21.7 degrees (SD 4.9 degrees) and 10.2 degrees (SD 4.6 degrees) 1 year following the reconstruction. The results for the pathologic knee were 64.5 degrees (SD 6.9 degrees), 20.6 degrees (SD 4.0 degrees) and 19.7 degrees (8.2 degrees) during the first evaluation, and 72.3 degrees (SD 2.4 degrees), 25.8 degrees (SD 6.4 degrees) and 12.4 degrees (SD 2.3 degrees) during the second one. The performance of the system enabled us to detect knee function modifications in the sagittal and transverse plane. Prior to the reconstruction, the ROM of the injured knee was lower in flexion-extension and internal-external rotation in comparison with the controlateral knee. One year after the surgery, four patients were classified normal (A) and one almost normal (B), according to the IKDC score, and changes in the kinematics of the five patients remained: lower flexion-extension ROM and higher internal-external rotation ROM in comparison with the controlateral knee. The 3D kinematics was changed after an ACL lesion and remained altered one year after the surgery