Abstract This study examines the impact of sustainability innovation on innovation performance and the mediating effects of knowledge sharing and knowledge application on this relationship. We hypothesize that small and medium-sized enterprises implementing sustainability innovation are able to achieve better innovation outcomes. We tested the hypotheses using structural equation modeling and Smart PLS on a data set of 185 industrial companies. The results support our conceptualization and demonstrate its utility in explaining improvements in achieving innovation performance if sustainability innovation is adopted. Simultaneously, we demonstrate that knowledge sharing and knowledge application mediate the effects of sustainability innovation on innovation performance. Findings have important implications regarding sustainability innovation implementation in industrial companies and its impact on improving innovation outcomes, both direct and mediated by knowledge sharing and knowledge application.

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.

Detecting malignant pulmonary nodules at an early stage can allow medical interventions which may increase the survival rate of lung cancer patients. Using computer vision techniques to detect nodules can improve the sensitivity and the speed of interpreting chest CT for lung cancer screening. Many studies have used CNNs to detect nodule candidates. Though such approaches have been shown to outperform the conventional image processing based methods regarding the detection accuracy, CNNs are also known to be limited to generalize on under-represented samples in the training set and prone to imperceptible noise perturbations. Such limitations can not be easily addressed by scaling up the dataset or the models. In this work, we propose to add adversarial synthetic nodules and adversarial attack samples to the training data to improve the generalization and the robustness of the lung nodule detection systems. To generate hard examples of nodules from a differentiable nodule synthesizer, we use projected gradient descent (PGD) to search the latent code within a bounded neighbourhood that would generate nodules to decrease the detector response. To make the network more robust to unanticipated noise perturbations, we use PGD to search for noise patterns that can trigger the network to give over-confident mistakes. By evaluating on two different benchmark datasets containing consensus annotations from three radiologists, we show that the proposed techniques can improve the detection performance on real CT data. To understand the limitations of both the conventional networks and the proposed augmented networks, we also perform stress-tests on the false positive reduction networks by feeding different types of artificially produced patches. We show that the augmented networks are more robust to both under-represented nodules as well as resistant to noise perturbations.

Recently image-to-image translation has attracted significant interests in the literature, starting from the successful use of the generative adversarial network (GAN), to the introduction of cyclic constraint, to extensions to multiple domains. However, in existing approaches, there is no guarantee that the mapping between two image domains is unique or one-to-one. Here we propose a self-inverse network learning approach for unpaired image-to-image translation. Building on top of CycleGAN, we learn a self-inverse function by simply augmenting the training samples by swapping inputs and outputs during training and with separated cycle consistency loss for each mapping direction. The outcome of such learning is a proven one-to-one mapping function. Our extensive experiments on a variety of datasets, including cross-modal medical image synthesis, object transfiguration, and semantic labeling, consistently demonstrate clear improvement over the CycleGAN method both qualitatively and quantitatively. Especially our proposed method reaches the state-of-the-art result on the cityscapes benchmark dataset for the label to photo un-paired directional image translation.

Robust and fast detection of anatomical structures is a prerequisite for both diagnostic and interventional medical image analysis. Current solutions for anatomy detection are typically based on machine learning techniques that exploit large annotated image databases in order to learn the appearance of the captured anatomy. These solutions are subject to several limitations, including the use of suboptimal feature engineering techniques and most importantly the use of computationally suboptimal search-schemes for anatomy detection. To address these issues, we propose a method that follows a new paradigm by reformulating the detection problem as a behavior learning task for an artificial agent. We couple the modeling of the anatomy appearance and the object search in a unified behavioral framework, using the capabilities of deep reinforcement learning and multi-scale image analysis. In other words, an artificial agent is trained not only to distinguish the target anatomical object from the rest of the body but also how to find the object by learning and following an optimal navigation path to the target object in the imaged volumetric space. We evaluated our approach on 1487 3D-CT volumes from 532 patients, totaling over 500,000 image slices and show that it significantly outperforms state-of-the-art solutions on detecting several anatomical structures with no failed cases from a clinical acceptance perspective, while also achieving a 20-30 percent higher detection accuracy. Most importantly, we improve the detection-speed of the reference methods by 2-3 orders of magnitude, achieving unmatched real-time performance on large 3D-CT scans.

Fast and robust detection of anatomical structures or pathologies represents a fundamental task in medical image analysis. Most of the current solutions are however suboptimal and unconstrained by learning an appearance model and exhaustively scanning the space of parameters to detect a specific anatomical structure. In addition, typical feature computation or estimation of meta-parameters related to the appearance model or the search strategy, is based on local criteria or predefined approximation schemes. We propose a new learning method following a fundamentally different paradigm by simultaneously modeling both the object appearance and the parameter search strategy as a unified behavioral task for an artificial agent. The method combines the advantages of behavior learning achieved through reinforcement learning with effective hierarchical feature extraction achieved through deep learning. We show that given only a sequence of annotated images, the agent can automatically and strategically learn optimal paths that converge to the sought anatomical landmark location as opposed to exhaustively scanning the entire solution space. The method significantly outperforms state-of-the-art machine learning and deep learning approaches both in terms of accuracy and speed on 2D magnetic resonance images, 2D ultrasound and 3D CT images, achieving average detection errors of 1-2 pixels, while also recognizing the absence of an object from the image. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Robust and fast solutions for anatomical object detection and segmentation support the entire clinical workflow from diagnosis, patient stratification, therapy planning, intervention and follow-up. Current state-of-the-art techniques for parsing volumetric medical image data are typically based on machine learning methods that exploit large annotated image databases. Two main challenges need to be addressed, these are the efficiency in scanning high-dimensional parametric spaces and the need for representative image features which require significant efforts of manual engineering. We propose a pipeline for object detection and segmentation in the context of volumetric image parsing, solving a two-step learning problem: anatomical pose estimation and boundary delineation. For this task we introduce Marginal Space Deep Learning (MSDL), a novel framework exploiting both the strengths of efficient object parametrization in hierarchical marginal spaces and the automated feature design of Deep Learning (DL) network architectures. In the 3D context, the application of deep learning systems is limited by the very high complexity of the parametrization. More specifically 9 parameters are necessary to describe a restricted affine transformation in 3D, resulting in a prohibitive amount of billions of scanning hypotheses. The mechanism of marginal space learning provides excellent run-time performance by learning classifiers in clustered, high-probability regions in spaces of gradually increasing dimensionality. To further increase computational efficiency and robustness, in our system we learn sparse adaptive data sampling patterns that automatically capture the structure of the input. Given the object localization, we propose a DL-based active shape model to estimate the non-rigid object boundary. Experimental results are presented on the aortic valve in ultrasound using an extensive dataset of 2891 volumes from 869 patients, showing significant improvements of up to 45.2% over the state-of-the-art. To our knowledge, this is the first successful demonstration of the DL potential to detection and segmentation in full 3D data with parametrized representations.

Robust and fast 3D tracking of deformable objects, such as heart, is a challenging task because of the relatively low image contrast and speed requirement. Many existing 2D algorithms might not be directly applied on the 3D tracking problem. The 3D tracking performance is limited due to dramatically increased data size, landmarks ambiguity, signal drop-out or complex nonrigid deformation. In this paper, we present a robust, fast, and accurate 3D tracking algorithm: prediction based collaborative trackers (PCT). A novel one-step forward prediction is introduced to generate the motion prior using motion manifold learning. Collaborative trackers are introduced to achieve both temporal consistency and failure recovery. Compared with tracking by detection and 3D optical flow, PCT provides the best results. The new tracking algorithm is completely automatic and computationally efficient. It requires less than 1.5 s to process a 3D volume which contains millions of voxels. In order to demonstrate the generality of PCT, the tracker is fully tested on three large clinical datasets for three 3D heart tracking problems with two different imaging modalities: endocardium tracking of the left ventricle (67 sequences, 1134 3D volumetric echocardiography data), dense tracking in the myocardial regions between the epicardium and endocardium of the left ventricle (503 sequences, roughly 9000 3D volumetric echocardiography data), and whole heart four chambers tracking (20 sequences, 200 cardiac 3D volumetric CT data). Our datasets are much larger than most studies reported in the literature and we achieve very accurate tracking results compared with human experts' annotations and recent literature.

As decisions in cardiology increasingly rely on noninvasive methods, fast and precise image processing tools have become a crucial component of the analysis workflow. To the best of our knowledge, we propose the first automatic system for patient-specific modeling and quantification of the left heart valves, which operates on cardiac computed tomography (CT) and transesophageal echocardiogram (TEE) data. Robust algorithms, based on recent advances in discriminative learning, are used to estimate patient-specific parameters from sequences of volumes covering an entire cardiac cycle. A novel physiological model of the aortic and mitral valves is introduced, which captures complex morphologic, dynamic, and pathologic variations. This holistic representation is hierarchically defined on three abstraction levels: global location and rigid motion model, nonrigid landmark motion model, and comprehensive aortic-mitral model. First we compute the rough location and cardiac motion applying marginal space learning. The rapid and complex motion of the valves, represented by anatomical landmarks, is estimated using a novel trajectory spectrum learning algorithm. The obtained landmark model guides the fitting of the full physiological valve model, which is locally refined through learned boundary detectors. Measurements efficiently computed from the aortic-mitral representation support an effective morphological and functional clinical evaluation. Extensive experiments on a heterogeneous data set, cumulated to 1516 TEE volumes from 65 4-D TEE sequences and 690 cardiac CT volumes from 69 4-D CT sequences, demonstrated a speed of 4.8 seconds per volume and average accuracy of 1.45 mm with respect to expert defined ground-truth. Additional clinical validations prove the quantification precision to be in the range of inter-user variability. To the best of our knowledge this is the first time a patient-specific model of the aortic and mitral valves is automatically estimated from volumetric sequences.

Recently, we proposed marginal space learning (MSL) as a generic approach for automatic detection of 3D anatomical structures in many medical imaging modalities. To accurately localize a 3D object, we need to estimate nine parameters (three for position, three for orientation, and three for anisotropic scaling). Instead of uniformly searching the original nine-dimensional parameter space, only low-dimensional marginal spaces are uniformly searched in MSL, which significantly improves the speed. In many real applications, a strong correlation may exist among parameters in the same marginal spaces. For example, a large object may have large scaling values along all directions. In this paper, we propose constrained MSL to exploit this correlation for further speed-up. As another major contribution, we propose to use quaternions for 3D orientation representation and distance measurement to overcome the inherent drawbacks of Euler angles in the original MSL. The proposed method has been tested on three 3D anatomical structure detection problems in medical images, including liver detection in computed tomography (CT) volumes, and left ventricle detection in both CT and ultrasound volumes. Experiments on the largest datasets ever reported show that constrained MSL can improve the detection speed up to 14 times, while achieving comparable or better detection accuracy. It takes less than half a second to detect a 3D anatomical structure in a volume.

Magnetic resonance imaging (MRI) is currently the gold standard for left ventricle (LV) quantification. Detection of the LV in an MRI image is a prerequisite for functional measurement. However, due to the large variations in the orientation, size, shape, and image intensity of the LV, automatic LV detection is challenging. In this paper, we propose to use marginal space learning (MSL) to exploit the recent advances in learning discriminative classifiers. Unlike full space learning (FSL) where a monolithic classifier is trained directly in the five dimensional object pose space (two for position, one for rotation, and two for anisotropic scaling), we train three detectors, namely, the position detector, the position-orientation detector, and the position-orientation-scale detector. As a contribution of this paper, we perform thorough comparison between MSL and FSL. Experiments show MSL significantly outperforms FSL on both the training and test sets. Additionally, we also detect several LV landmarks, such as the LV apex and two annulus points. If we combine the detected candidates from both the whole-object detector and landmark detectors, we can further improve the system robustness even when one detector fails. A novel ranking-based strategy is proposed to combine the detected candidates from all detectors. Experiments show our ranking-based aggregation approach can significantly reduce the detection outliers.

In this paper we propose a robust and efficient approach to localizing and estimating the motion of non-rigid and articulated objects using marginal trajectory spectrum learning. Detecting the motion directly in the Euclidean space is often found difficult to guarantee a smooth and accurate result and might be affected by drifting. These issues, however, can be addressed effectively by formulating the motion estimation problem as spectrum detection in the trajectory space. The full trajectory space can be decomposed into orthogonal subspaces defined by generic bases, such as the Discrete Fourier Transform (DFT). The obtained representation is shown to be compact, facilitating efficient learning and optimization in its marginal spaces. In the training stage, local features are extended in the temporal domain to integrate the time coherence constraint and selected via boosting to form strong classifiers. An incremental optimization is performed in sparse marginal spaces learned from the training data. To maximize efficiency and robustness we constrain the search based on clusters of hypotheses defined in each subspace. Experiments demonstrate the performance of the proposed method on articulated motion estimation of aortic and mitral valves from ultrasound data. Our method is evaluated on 65 4D TEE sequences (1516 volumes) with the accuracy in the range of the inter-user variability of expert users. It provides in less than 60 seconds with an precision of 1.36 ± 0.32mm a personalized 4D model of aortic and mitral valves crucial for the clinical workflow.

The use of 3-D ultrasound data has several advantages over 2-D ultrasound for fetal biometric measurements, such as considerable decrease in the examination time, possibility of post-exam data processing by experts and the ability to produce 2-D views of the fetal anatomies in orientations that cannot be seen in common 2-D ultrasound exams. However, the search for standardized planes and the precise localization of fetal anatomies in ultrasound volumes are hard and time consuming processes even for expert physicians and sonographers. The relative low resolution in ultrasound volumes, small size of fetus anatomies and inter-volume position, orientation and size variability make this localization problem even more challenging. In order to make the plane search and fetal anatomy localization problems completely automatic, we introduce a novel principled probabilistic model that combines discriminative and generative classifiers with contextual information and sequential sampling. We implement a system based on this model, where the user queries consist of semantic keywords that represent anatomical structures of interest. After queried, the system automatically displays standardized planes and produces biometric measurements of the fetal anatomies. Experimental results on a held-out test set show that the automatic measurements are within the inter-user variability of expert users. It resolves for position, orientation and size of three different anatomies in less than 10 seconds in a dual-core computer running at 1.7 GHz.

In this paper we present a hierarchical, learning-based approach for automatic and accurate liver segmentation from 3D CT volumes. We target CT volumes that come from largely diverse sources (e.g., diseased in six different organs) and are generated by different scanning protocols (e.g., contrast and non-contrast, various resolution and position). Three key ingredients are combined to solve the segmentation problem. First, a hierarchical framework is used to efficiently and effectively monitor the accuracy propagation in a coarse-to-fine fashion. Second, two new learning techniques, marginal space learning and steerable features, are applied for robust boundary inference. This enables handling of highly heterogeneous texture pattern. Third, a novel shape space initialization is proposed to improve traditional methods that are limited to similarity transformation. The proposed approach is tested on a challenging dataset containing 174 volumes. Our approach not only produces excellent segmentation accuracy, but also runs about fifty times faster than state-of-the-art solutions [7, 9].

Tracking the left ventricle (LV) in 3D ultrasound data is a challenging task because of the poor image quality and speed requirements. Many previous algorithms applied standard 2D tracking methods to tackle the 3D problem. However, the performance is limited due to increased data size, landmarks ambiguity, signal drop-out or non-rigid deformation. In this paper we present a robust, fast and accurate 3D LV tracking algorithm. We propose a novel one-step forward prediction to generate the motion prior using motion manifold learning, and introduce two collaborative trackers to achieve both temporal consistency and failure recovery. Compared with tracking by detection and 3D optical flow, our algorithm provides the best results and sub-voxel accuracy. The new tracking algorithm is completely automatic and computationally efficient. It requires less than 1.5 seconds to process a 3D volume which contains 4,925,440 voxels.

We propose a novel method for the automatic detection and measurement of fetal anatomical structures in ultrasound images. This problem offers a myriad of challenges, including: difficulty of modeling the appearance variations of the visual object of interest, robustness to speckle noise and signal dropout, and large search space of the detection procedure. Previous solutions typically rely on the explicit encoding of prior knowledge and formulation of the problem as a perceptual grouping task solved through clustering or variational approaches. These methods are constrained by the validity of the underlying assumptions and usually are not enough to capture the complex appearances of fetal anatomies. We propose a novel system for fast automatic detection and measurement of fetal anatomies that directly exploits a large database of expert annotated fetal anatomical structures in ultrasound images. Our method learns automatically to distinguish between the appearance of the object of interest and background by training a constrained probabilistic boosting tree classifier. This system is able to produce the automatic segmentation of several fetal anatomies using the same basic detection algorithm. We show results on fully automatic measurement of biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL), humerus length (HL), and crown rump length (CRL). Notice that our approach is the first in the literature to deal with the HL and CRL measurements. Extensive experiments (with clinical validation) show that our system is, on average, close to the accuracy of experts in terms of segmentation and obstetric measurements. Finally, this system runs under half second on a standard dual-core PC computer.

We propose an automatic four-chamber heart segmentation system for the quantitative functional analysis of the heart from cardiac computed tomography (CT) volumes. Two topics are discussed: heart modeling and automatic model fitting to an unseen volume. Heart modeling is a nontrivial task since the heart is a complex nonrigid organ. The model must be anatomically accurate, allow manual editing, and provide sufficient information to guide automatic detection and segmentation. Unlike previous work, we explicitly represent important landmarks (such as the valves and the ventricular septum cusps) among the control points of the model. The control points can be detected reliably to guide the automatic model fitting process. Using this model, we develop an efficient and robust approach for automatic heart chamber segmentation in 3-D CT volumes. We formulate the segmentation as a two-step learning problem: anatomical structure localization and boundary delineation. In both steps, we exploit the recent advances in learning discriminative models. A novel algorithm, marginal space learning (MSL), is introduced to solve the 9-D similarity transformation search problem for localizing the heart chambers. After determining the pose of the heart chambers, we estimate the 3-D shape through learning-based boundary delineation. The proposed method has been extensively tested on the largest dataset (with 323 volumes from 137 patients) ever reported in the literature. To the best of our knowledge, our system is the fastest with a speed of 4.0 s per volume (on a dual-core 3.2-GHz processor) for the automatic segmentation of all four chambers.

In this paper we present a method for learning a curve model for detection and segmentation by closely integrating a hierarchical curve representation using generative and discriminative models with a hierarchical inference algorithm. We apply this method to the problem of automatic localization of the guidewire in fluoroscopic sequences. In fluoroscopic sequences, the guidewire appears as a hardly visible, non-rigid one-dimensional curve. Our paper has three main contributions. Firstly, we present a novel method to learn the complex shape and appearance of a free-form curve using a hierarchical model of curves of increasing degrees of complexity and a database of manual annotations. Secondly, we present a novel computational paradigm in the context of Marginal Space Learning, in which the algorithm is closely integrated with the hierarchical representation to obtain fast parameter inference. Thirdly, to our knowledge this is the first full system which robustly localizes the whole guidewire and has extensive validation on hundreds of frames. We present very good quantitative and qualitative results on real fluoroscopic video sequences, obtained in just one second per frame.

Multi-chamber heart segmentation is a prerequisite for global quantification of the cardiac function. The complexity of cardiac anatomy, poor contrast, noise or motion artifacts makes this segmentation problem a challenging task. In this paper, we present an efficient, robust, and fully automatic segmentation method for 3D cardiac computed tomography (CT) volumes. Our approach is based on recent advances in learning discriminative object models and we exploit a large database of annotated CT volumes. We formulate the segmentation as a two step learning problem: anatomical structure localization and boundary delineation. A novel algorithm, marginal space learning (MSL), is introduced to solve the 9-dimensional similarity search problem for localizing the heart chambers. MSL reduces the number of testing hypotheses by about six orders of magnitude. We also propose to use steerable image features, which incorporate the orientation and scale information into the distribution of sampling points, thus avoiding the time-consuming volume data rotation operations. After determining the similarity transformation of the heart chambers, we estimate the 3D shape through learning-based boundary delineation. Extensive experiments on multi-chamber heart segmentation demonstrate the efficiency and robustness of the proposed approach, comparing favorably to the state-of-the-art. This is the first study reporting stable results on a large cardiac CT dataset with 323 volumes. In addition, we achieve a speed of less than eight seconds for automatic segmentation of all four chambers.

Many natural objects vary the shapes as linear combinations of certain bases. The measurement of such deformable shapes is coupling of rigid similarity transformations between the objects and the measuring systems and non-rigid deformations controlled by the linear bases. Thus registration and modeling of deformable shapes are coupled problems, where registration is to compute the rigid transformations and modeling is to construct the linear bases. The previous methods [3, 2] separate the solution into two steps. The first step registers the measurements regarding the shapes as rigid and the deformations as random noise. The second step constructs the linear model using the registered shapes. Since the deformable shapes do not vary randomly but are constrained by the underlying model, such separate steps result in registration biased by nonrigid deformations and shape models involving improper rigid transformations. We for the first time present this bias problem and formulate that, the coupled registration and modeling problems are essentially a single factorization problem and thus require a simultaneous solution. We then propose the Direct Factorization method that extends a structure from motion method [16]. It yields a linear closedform solution that simultaneously registers the deformable shapes at arbitrary dimensions (2D \to 2D, 3D \to 3D, . . .) and constructs the linear bases. The accuracy and robustness of the proposed approach are demonstrated quantitatively on synthetic data and qualitatively on real shapes.

Motion estimation for applications where appearance undergoes complex changes is challenging due to lack of an appropriate similarity function. In this paper, we propose to learn a discriminative similarity function based on an annotated database that exemplifies the appearance variations. We invoke the LogitBoost algorithm to selectively combine weak learners into one strong similarity function. The weak learners based on local rectangle features are constructed as nonparametric 2D piecewise constant functions, using the feature responses from both images, to strengthen the modeling power and accommodate fast evaluation. Because the negatives possess a location parameter measuring their closeness to the positives, we present a locationsensitive cascade training procedure, which bootstraps negatives for later stages of the cascade from the regions closer to the positives. This allows viewing a large number of negatives and steering the training process to yield lower training and test errors. In experiments of estimating the motion for the endocardial wall of the left ventricle in echocardiography, we compare the learned similarity function with conventional ones and obtain improved performances. We also contrast the proposed method with a learning-based detection algorithm to demonstrate the importance of temporal information in motion estimation. Finally, we insert the learned similarity function into a simple contour tracking algorithm and find that it reduces drifting.

We tackle the problem of automatically classifying cardiac view for an echocardiographic sequence as a multiclass object detection. As a solution, we present an imagebased multiclass boosting procedure. In contrast with conventional approaches for multiple object detection that train multiple binary classifiers, one per object, we learn only one multiclass classifier using the LogitBoosting algorithm. To utilize the fact that, in the midst of boosting, one class is fully separated from the remaining classes, we propose to learn a tree structure that focuses on the remaining classes to improve learning efficiency. Further, we accommodate the large number of background images using a cascade of boosted multiclass classifiers, which is able to simultaneously detect and classify multiple objects while rejecting the background class quickly. Our experiments on echocardiographic view classification demonstrate promising performances of image-based multiclass boosting.

The segmentation of anatomical structures has been traditionally formulated as a perceptual grouping task, and solved through clustering and variational approaches. However, such strategies require the a priori knowledge to be explicitly defined in the optimization criterion, e.g., "high-gradient border", "smoothness"', or "similar intensity or texture". This approach is limited by the validity of underlying assumptions and cannot capture complex structure appearance. This paper introduces database-guided segmentation as a new data-driven paradigm that directly exploits expert annotation of interest structures in large medical databases. Segmentation is formulated as a two-step learning problem. The first step is structure detection where we learn how to discriminate between the object of interest and background. The resulting classifier based on a boosted cascade of simple features also provides a global rigid transformation of the structure. The second step is shape inference where we use a sample-based representation of the joint distribution of appearance and shape annotations. To learn the association between the complex appearance and shape we propose a feature selection mechanism and the corresponding metric. We show that the selected features are better than using directly the appearance and illustrate the performance of the proposed method on a large set of ultrasound heart images.

We present a general algorithm of image based regression that is applicable to many vision problems. The proposed regressor that targets a multiple-output setting is learned using boosting method. We formulate a multiple-output regression problem in such a way that overfitting is decreased and an analytic solution is admitted. Because we represent the image via a set of highly redundant Haar-like features that can be evaluated very quickly and select relevant features through boosting to absorb the knowledge of the training data, during testing we require no storage of the training data and evaluate the regression function almost in no time. We also propose an efficient training algorithm that breaks the computational bottleneck in the greedy feature selection process. We validate the efficiency of the proposed regressor using three challenging tasks of age estimation, tumor detection, and endocardial wall localization and achieve the best performance with a dramatic speed, e.g., more than 1000 times faster than conventional data-driven techniques such as support vector regressor in the experiment of endocardial wall localization.

To solve the general point correspondence problem in which the underlying transformation between image patches is represented by a homography, a solution based on extensive use of first order differential techniques is proposed. We integrate in a single robust M-estimation framework the traditional optical flow method and matching of local color distributions. These distributions are computed with spatially oriented kernels in the 5D joint spatial/color space. The estimation process is initiated at the third level of a Gaussian pyramid, uses only local information, and the illumination changes between the two images are also taken into account. Subpixel matching accuracy is achieved under large projective distortions significantly exceeding the performance of any of the two components alone. As an application, the correspondence algorithm is employed in oriented tracking of objects.

Feature space analysis is the main module in many computer vision tasks. The most popular technique, k-means clustering, however, has two inherent limitations: the clusters are constrained to be spherically symmetric and their number has to be known a priori. In nonparametric clustering methods, like the one based on mean shift, these limitations are eliminated but the amount of computation becomes prohibitively large as the dimension of the space increases. We exploit a recently proposed approximation technique, locality-sensitive hashing (LSH), to reduce the computational complexity of adaptive mean shift. In our implementation of LSH the optimal parameters of the data structure are determined by a pilot learning procedure, and the partitions are data driven. As an application, the performance of mode and k-means based textons are compared in a texture classification study.

Reliable estimation of the trifocal tensor is crucial for 3D reconstruction from uncalibrated cameras. The estimation process is based on minimizing the geometric distances between the measurements and the corrected data points, the underlying nonlinear optimization problem being most often solved with the Levenberg-Marquardt (LM) algorithm. We employ for this task the heteroscedastic errors-in-variables (HEIV) estimator and take into account both the singularity of the multivariate tensor constraint and the bifurcation which can appear for noisy data. In comparison to the Gold Standard method, the new approach is significantly faster while having the same performance, and it is less sensitive to initialization when the data is close to degenerate. Analytical expressions for the covariances of the parameter and corrected image point estimates are available for the HEIV estimator and thus the confidence regions of the corrected measurements can be delineated in the images.

Computing the weighted average of the pixel values in a window is a basic module in many computer vision operators. The process is reformulated in a linear vector space and the role of the different subspaces is emphasized. Within this framework wellknown artifacts of the gradient-based edge detectors, such as large spurious responses can be explained quantitatively. It is also shown that template matching with a template derived from the input data is meaningful since it provides an independent measure of confidence in the presence of the employed edge model. The widely used three-step edge detection procedure - gradient estimation, non-maxima suppression, hysteresis thresholding - is generalized to include the information provided by the confidence measure. The additional amount of computation is minimal and experiments with several standard test images show the ability of the new procedure to detect weak edges.

The features employed in content-based retrieval are most often simple low-level representations, while a human observer judges similarity between images based on high-level semantic properties. Using textures as an example, we show that a more accurate description of the underlying distribution of low-level features does not improve the retrieval performance. We also introduce the simplified multiresolution symmetric autoregressive model for textures, and the Bhattacharyya distance based similarity measure. Experiments are performed with four texture representations and four similarity measures over the Brodatz and Vis Tex databases.

Recent advances in networking, robotics and computer technology allow real-time diagnosis, consultation, and education by using images obtained through remote microscopy. This paper presents a new approach in telepathology, the image guided decision support (IGDS) system, which integrates components for both remote microscope control and decision support. Using the micro-controller component the physician can command a robotic microscope from a distance, obtain high-quality images to be used in the diagnosis, and authorize other users to visualize the same images. The image understanding-based decision support component of the system locates, retrieves and displays cases which exhibit morphological profiles consistent to the case in question and suggests the most likely diagnosis based on majority logic. The IGDS system has a natural man-machine interface containing engines for speech recognition and voice feedback.