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PubMed1996

Detection of non-topological motifs in protein structures.

We present an efficient technique for the comparison of protein structures. The algorithm uses a vector representation of the secondary structure elements and searches for spatial configurations of secondary structure elements in proteins. In such recurring protein folds, the order of the secondary structure elements in the protein chains is disregarded. The method is based on the geometric hashing paradigm and implements approaches originating in computer vision. It represents and matches the secondary structure element vectors in a 3-D translation and rotation invariant manner. The matching of a pair of proteins takes on average under 3 s on a Silicon Graphics Indigo2 workstation, allowing extensive all-against-all comparisons of the data set of non-redundant protein structures. Here we have carried out such a comparison for a data set of over 500 protein molecules. The detection of recurring topological and non-topological, secondary structure element order-independent protein folds may provide further insight into evolution. Moreover, as these recurring folding units are likely to be conformationally favourable, the availability of a data set of such topological motifs can serve as a rich input for threading routines. Below, we describe this rapid technique and the results it has obtained. While some of the obtained matches conserve the order of the secondary structure elements, others are entirely order independent. As an example, we focus on the results obtained for Che Y, a signal transduction protein, and on the profilin-beta-actin complex. The Che Y molecule is composed of a five-stranded, parallel beta-sheet flanked by five helices. Here we show its similarity with the Escherichia coli elongation factor, with L-arabinose binding protein, with haloalkane dehalogenase and with adenylate kinase. The profilin-beta-actin contains an antiparallel beta-pleated sheet with alpha-helical termini. Its similarities to lipase, fructose disphosphatase and beta-lactamase are displayed.

Algorithms↗

PubMed2004

Mutual information in coupled multi-shape model for medical image segmentation.

This paper presents extensions which improve the performance of the shape-based deformable active contour model presented earlier in [IEEE Conf. Comput. Vision Pattern Recog. 1 (2001) 463] for medical image segmentation. In contrast to that previous work, the segmentation framework that we present in this paper allows multiple shapes to be segmented simultaneously in a seamless fashion. To achieve this, multiple signed distance functions are employed as the implicit representations of the multiple shape classes within the image. A parametric model for this new representation is derived by applying principal component analysis to the collection of these multiple signed distance functions. By deriving a parametric model in this manner, we obtain a coupling between the multiple shapes within the image and hence effectively capture the co-variations among the different shapes. The parameters of the multi-shape model are then calculated to minimize a single mutual information-based cost criterion for image segmentation. The use of a single cost criterion further enhances the coupling between the multiple shapes as the deformation of any given shape depends, at all times, upon every other shape, regardless of their proximity. We found that this resulting algorithm is able to effectively utilize the co-dependencies among the different shapes to aid in the segmentation process. It is able to capture a wide range of shape variability despite being a parametric shape-model. And finally, the algorithm is robust to large amounts of additive noise. We demonstrate the utility of this segmentation framework by applying it to a medical application: the segmentation of the prostate gland, the rectum, and the internal obturator muscles for MR-guided prostate brachytherapy.

Algorithms↗

PubMed2006

Error analysis of robust optical flow estimation by least median of squares methods for the varying illumination model.

The apparent pixel motion in an image sequence, called optical flow, is a useful primitive for automatic scene analysis and various other applications of computer vision. In general, however, the optical flow estimation suffers from two significant problems: the problem of illumination that varies with time and the problem of motion discontinuities induced by objects moving with respect to either other objects or with respect to the background. Various integrated approaches for solving these two problems simultaneously have been proposed. Of these, those that are based on the LMedS (Least Median of Squares) appear to be the most robust. The goal of this paper is to carry out an error analysis of two different LMedS-based approaches, one based on the standard LMedS regression and the other using a modification thereof as proposed by us recently. While it is to be expected that the estimation accuracy of any approach would decrease with increasing levels of noise, for LMedS-like methods, it is not always clear as to how much of that decrease in performance can be attributed to the fact that only a small number of randomly selected samples is used for forming temporary solutions. To answer this question, our study here includes a baseline implementation in which all of the image data is used for forming motion estimates. We then compare the estimation errors of the two LMedS-based methods with the baseline implementation. Our error analysis demonstrates that, for the case of Gaussian noise, our modified LMedS approach yields better estimates at moderate levels of noise, but is outperformed by the standard LMedS method as the level of noise increases. For the case of salt-and-pepper noise, the modified LMedS method consistently performs better than the standard LMedS method.

Algorithms↗

PubMed2005

Metric 3D reconstruction and texture acquisition of surfaces of revolution from a single uncalibrated view.

Image analysis and computer vision can be effectively employed to recover the three-dimensional structure of imaged objects, together with their surface properties. In this paper, we address the problem of metric reconstruction and texture acquisition from a single uncalibrated view of a surface of revolution (SOR). Geometric constraints induced in the image by the symmetry properties of the SOR structure are exploited to perform self-calibration of a natural camera, 3D metric reconstruction, and texture acquisition. By exploiting the analogy with the geometry of single axis motion, we demonstrate that the imaged apparent contour and the visible segments of two imaged cross sections in a single SOR view provide enough information for these tasks. Original contributions of the paper are: single view self-calibration and reconstruction based on planar rectification, previously developed for planar surfaces, has been extended to deal also with the SOR class of curved surfaces; self-calibration is obtained by estimating both camera focal length (one parameter) and principal point (two parameters) from three independent linear constraints for the SOR fixed entities; the invariant-based description of the SOR scaling function has been extended from affine to perspective projection. The solution proposed exploits both the geometric and topological properties of the transformation that relates the apparent contour to the SOR scaling function. Therefore, with this method, a metric localization of the SOR occluded parts can be made, so as to cope with them correctly. For the reconstruction of textured SORs, texture acquisition is performed without requiring the estimation of external camera calibration parameters, but only using internal camera parameters obtained from self-calibration.

Algorithms↗

PubMed2001

Automated tracking of facial features in patients with facial neuromuscular dysfunction.

Facial neuromuscular dysfunction severely impacts adaptive and expressive behavior and emotional health. Appropriate treatment is aided by quantitative and efficient assessment of facial motion impairment. We validated a newly developed method of quantifying facial motion, automated face analysis (AFA), by comparing it with an established manual marking method, the Maximal Static Response Assay (MSRA). In the AFA, motion of facial features is tracked automatically by computer vision without the need for placement of physical markers or restrictions of rigid head motion. Nine patients (seven women and two men) with a mean age of 39.3 years and various facial nerve disorders (five with Bell's palsy, three with trauma, and one with tumor resection) participated. The patients were videotaped while performing voluntary facial action tasks (brow raise, eye closure, and smile). For comparison with MSRA, physical markers were placed on facial landmarks. Image sequences were digitized into 640 x 480 x 24-bit pixel arrays at 30 frames per second (1 pixel congruent with0.3 mm). As defined for the MSRA, the coordinates of the center of each marker were manually recorded in the initial and final digitized frames, which correspond to repose and maximal response. For the AFA, these points were tracked automatically in the image sequence. Pearson correlation coefficients were used to evaluate consistency of measurement between manual (the MSRA) and automated (the AFA) tracking methods, and paired t tests were used to assess the mean difference between methods for feature tracking. Feature measures were highly consistent between methods, Pearson's r = 0.96 or higher, p < 0.001 for each of the action tasks. The mean differences between the methods were small; the mean error between methods was comparable to the error within the manual method (less than 1 pixel). The AFA demonstrated strong concurrent validity with the MSRA for pixel-wise displacement. Tracking was fully automated and provided motion vectors, which may be useful in guiding surgical and rehabilitative approaches to restoring facial function in patients with facial neuromuscular disorders.

Adult↗

PubMed2004

A review of content-based image retrieval systems in medical applications-clinical benefits and future directions.

Content-based visual information retrieval (CBVIR) or content-based image retrieval (CBIR) has been one on the most vivid research areas in the field of computer vision over the last 10 years. The availability of large and steadily growing amounts of visual and multimedia data, and the development of the Internet underline the need to create thematic access methods that offer more than simple text-based queries or requests based on matching exact database fields. Many programs and tools have been developed to formulate and execute queries based on the visual or audio content and to help browsing large multimedia repositories. Still, no general breakthrough has been achieved with respect to large varied databases with documents of differing sorts and with varying characteristics. Answers to many questions with respect to speed, semantic descriptors or objective image interpretations are still unanswered. In the medical field, images, and especially digital images, are produced in ever-increasing quantities and used for diagnostics and therapy. The Radiology Department of the University Hospital of Geneva alone produced more than 12,000 images a day in 2002. The cardiology is currently the second largest producer of digital images, especially with videos of cardiac catheterization ( approximately 1800 exams per year containing almost 2000 images each). The total amount of cardiologic image data produced in the Geneva University Hospital was around 1 TB in 2002. Endoscopic videos can equally produce enormous amounts of data. With digital imaging and communications in medicine (DICOM), a standard for image communication has been set and patient information can be stored with the actual image(s), although still a few problems prevail with respect to the standardization. In several articles, content-based access to medical images for supporting clinical decision-making has been proposed that would ease the management of clinical data and scenarios for the integration of content-based access methods into picture archiving and communication systems (PACS) have been created. This article gives an overview of available literature in the field of content-based access to medical image data and on the technologies used in the field. Section 1 gives an introduction into generic content-based image retrieval and the technologies used. Section 2 explains the propositions for the use of image retrieval in medical practice and the various approaches. Example systems and application areas are described. Section 3 describes the techniques used in the implemented systems, their datasets and evaluations. Section 4 identifies possible clinical benefits of image retrieval systems in clinical practice as well as in research and education. New research directions are being defined that can prove to be useful. This article also identifies explanations to some of the outlined problems in the field as it looks like many propositions for systems are made from the medical domain and research prototypes are developed in computer science departments using medical datasets. Still, there are very few systems that seem to be used in clinical practice. It needs to be stated as well that the goal is not, in general, to replace text-based retrieval methods as they exist at the moment but to complement them with visual search tools.

Databases, Factual↗

PubMed2005

Retinal image analysis: concepts, applications and potential.

As digital imaging and computing power increasingly develop, so too does the potential to use these technologies in ophthalmology. Image processing, analysis and computer vision techniques are increasing in prominence in all fields of medical science, and are especially pertinent to modern ophthalmology, as it is heavily dependent on visually oriented signs. The retinal microvasculature is unique in that it is the only part of the human circulation that can be directly visualised non-invasively in vivo, readily photographed and subject to digital image analysis. Exciting developments in image processing relevant to ophthalmology over the past 15 years includes the progress being made towards developing automated diagnostic systems for conditions, such as diabetic retinopathy, age-related macular degeneration and retinopathy of prematurity. These diagnostic systems offer the potential to be used in large-scale screening programs, with the potential for significant resource savings, as well as being free from observer bias and fatigue. In addition, quantitative measurements of retinal vascular topography using digital image analysis from retinal photography have been used as research tools to better understand the relationship between the retinal microvasculature and cardiovascular disease. Furthermore, advances in electronic media transmission increase the relevance of using image processing in 'teleophthalmology' as an aid in clinical decision-making, with particular relevance to large rural-based communities. In this review, we outline the principles upon which retinal digital image analysis is based. We discuss current techniques used to automatically detect landmark features of the fundus, such as the optic disc, fovea and blood vessels. We review the use of image analysis in the automated diagnosis of pathology (with particular reference to diabetic retinopathy). We also review its role in defining and performing quantitative measurements of vascular topography, how these entities are based on 'optimisation' principles and how they have helped to describe the relationship between systemic cardiovascular disease and retinal vascular changes. We also review the potential future use of fundal image analysis in telemedicine.

Animals↗

PubMed2005

A wrapper-based approach to image segmentation and classification.

The traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest from the background image. It is extremely difficult to obtain a reliable segmentation without any prior knowledge about the object that is being extracted from the scene. This is further complicated by the lack of any clearly defined metrics for evaluating the quality of segmentation or for comparing segmentation algorithms. We propose a method of segmentation that addresses both of these issues, by using the object classification subsystem as an integral part of the segmentation. This will provide contextual information regarding the objects to be segmented, as well as allow us to use the probability of correct classification as a metric to determine the quality of the segmentation. We view traditional segmentation as a filter operating on the image that is independent of the classifier, much like the filter methods for feature selection. We propose a new paradigm for segmentation and classification that follows the wrapper methods of feature selection. Our method wraps the segmentation and classification together, and uses the classification accuracy as the metric to determine the best segmentation. By using shape as the classification feature, we are able to develop a segmentation algorithm that relaxes the requirement that the object of interest to be segmented must be homogeneous in some low-level image parameter, such as texture, color, or grayscale. This represents an improvement over other segmentation methods that have used classification information only to modify the segmenter parameters, since these algorithms still require an underlying homogeneity in some parameter space. Rather than considering our method as, yet, another segmentation algorithm, we propose that our wrapper method can be considered as an image segmentation framework, within which existing image segmentation algorithms may be executed. We show the performance of our proposed wrapper-based segmenter on real-world and complex images of automotive vehicle occupants for the purpose of recognizing infants on the passenger seat and disabling the vehicle airbag. This is an interesting application for testing the robustness of our approach, due to the complexity of the images, and, consequently, we believe the algorithm will be suitable for many other real-world applications.

Algorithms↗

PubMed2000

Division of labour and seasonality in the ant Leptothorax albipennis: worker corpulence and its influence on behaviour.

We address the organization of workers in social insect societies. We distinguish between changes in behavioural role over the nurse to forager role sequence, which may depend on changes in physiology, and potentially more rapid changes of task within role. We investigated the association between role and nutrient status in the ant Leptothorax albipennis. Worker lipid stores were quantified using a new body size-controlled method, and were related to worker behaviour. Worker lipid stores were evenly distributed amongst colony members at the end of winter, splitting rapidly into two distinct modes (replete nurses and lean foragers) in spring. The proportion of lean foragers increased throughout spring and summer, until most colonies contained only workers of this type. Callow workers then eclosed with intermediate lipid stores. We developed a computer vision system that tracks all nest ants to extract detailed behaviour of individuals of known lipid stores. Lipid storage was negatively correlated with a worker's foraging propensity, and with measures of spatial occupation in the nest and of activity. Different colonies showed a similar quantitative correlation between lipid stores and behavioural role, suggesting that lipid stores were not only correlated with the relative organization of individuals within each nest, but may also have influenced their absolute role. We reviewed the literature and found evidence that nutrient status influences role predisposition in social insect workers. We conclude that the distribution of worker roles may be linked to the balance between foraging income and energetic consumption within the colony directly via worker nutrient status. Copyright 2000 The Association for the Study of Animal Behaviour.

Journal Article↗

PubMed1996

A dataset of protein-protein interfaces generated with a sequence-order-independent comparison technique.

While there are a number of structurally non-redundant datasets of protein monomers, there is none of protein-protein interfaces. Yet, the availability of such a dataset is expected to provide an added insight into a number of investigations. First and foremost among these is analyzing the interfaces to obtain their prevailing architectures, the forces that account for the protein-protein associations and their packing considerations. Their comparisons with those of the monomers are likely to shed additional light on protein-protein recognition on the one hand and on the folding of the polypeptide chain on the other. Docking simulations are also expected to benefit from the existence of such a dataset. A major stumbling block to the generation of a dataset of interfaces has been that the interface is composed of at least two chains. Furthermore, in the interfaces, each of the chains might be represented by non-contiguous pieces. Their order in the interfaces being compared might be different as well. This discontinuity stems from the definition of an interface. An interface consists of interacting residues between the chains, and those that are in their vicinity in the supporting scaffold, within a certain distance threshold. This necessarily yields unordered fragments, as well as isolated residues. Our novel, efficient, sequence-order-independent structural comparison technique is ideally suited to handle the task of the generation of a library of structurally non-redundant protein-protein interfaces. As it is computer-vision based, it views atoms as collections of points in space, disregarding their chain connectivity. In this work, 351 interface-families are created. Comparisons of the interfaces, and separately, of the chains which contribute to them, yield some interesting cases. In one of the cases, while two interfaces are similar, the structure of only one of the two chains is similar between the two complexes. The structure of the second chain of the first complex differs from that of the second chain of the second complex. Here the structure of the cleft in the first chain dictates the specific binding interactions. In another case, while the interfaces in the two complexes are similar, both chains composing them differ between the complexes. Lastly, the chains composing the complexes are similar, but the interfaces are dissimilar, providing a set of data for investigations of the favorable orientations of protein-protein associations.

Algorithms↗

PubMed1996

Recognition-by-parts: a computational approach to human learning and generalization of shapes.

In this paper human pattern recognition is modeled in terms of how human observers learn to describe patterns in terms of their perceived parts, their unary (part) and binary (relational) attributes and the way in which such attribute states "evidence' different classes of shapes. This approach, originally developed in the area of computer vision, is concerned with algorithms which enable the learning of shape descriptions from examples and the classification of new data (generalization) efficiently and accurately. An object in such an "evidence-based' system is represented by a set of rules, where each rule provides a certain amount of evidence for each object class in the database. The accumulated class evidence over all activated rules can then be used to determine the classification probability. We have examined how well this model reflects human perception by training observers to classify compound Gabor patterns and then testing them with versions of such patterns which were segmented (gray-level transformed) versions of the original training set. If the observers were to construct rules to define each pattern class in terms of perceived parts and their relations, then it should be expected that classification performance would generalize to these new patterns from the original set. Results confirm this hypothesis and the specific feature extraction, learning and rule generation model used to predict performance.

Attention↗

PubMed2001

A fast method for calibrating video-based motion analysers using only a rigid bar.

Video-camera systems are widely used in biomechanics and clinical fields to measure the 3D kinematic measurements of human motion. To be used, they need to be calibrated, that is the parameters which geometrically define the cameras have to be determined. It is shown here how this can be achieved by surveying a rigid bar in motion inside the working volume, and in a very short time: less than 15 s on a Pentium III. The exterior parameters are estimated through the coplanarity constraint, the camera focal lengths through the properties of epipolar geometry and the principal points with a fast evolutionary optimisation which guarantees convergence when the initial principal points cannot be adequately estimated. The method has been widely tested on simulated and real data. Results show that its accuracy is comparable with that obtained using methods based on points of known 3D coordinates (DLT): 0.37 mm RMS error over a volume with a diagonal approximately 1.5 m. A preferential absolute reference system is obtained from the same bar motion data and is used to guide an intelligent decimation of the data. Finally, the role that the principal points play in achieving a high accuracy, which is questioned in the computer vision domain, is assessed through simulations.

Biomechanical Phenomena↗

PubMed2005

Primary lymphoma of the skull base.

This case demonstrates the rare finding of a primary malignant lymphoma of the frontobasis and ethmoidal cells in a patient presenting with progressive loss of vision. Computed tomography and MRI demonstrated a homogenously enhancing tumour with extensive bony destructions of the skull base. After transsphenoidal biopsy, histology revealed a highly malignant primary B-cell lymphoma. The patient was further treated with immunochemotherapy. Tissue diagnosis is, therefore, crucial before a definitive therapy is instituted.

Biopsy↗

PubMed1989

Epithelium-lined cyst of the pretectal region: case report and electron microscope study.

Ultrastructural findings of an epithelium-lined cyst in the left pretectal region are reported. A 38-year-old woman developed a sensory disturbance on her right side and blurring of vision. Computed tomography scans and magnetic resonance images disclosed a round cystic lesion in the left pretectum. Light microscopically, the cyst was found to be lined by a single layer of cuboidal epithelial cells. Electron microscope examination revealed that the epithelial cells of the cyst possessed clear nuclei, abundant tonofilaments, and glycogen granules, featured as well as the usual organellae. The free surface of the epithelial cells had numerous finger-like microvilli with coating materials. These cells were interconnected by well-developed desmosomes and interdigitations, and also possessed basal lamina materials on the basal surface. These cytological features suggest a heterogenous origin of the cells rather than a neuroepithelial one.

Adult↗

PubMed1992

Cranial fasciitis of childhood: a case report.

Cranial fasciitis of childhood is very rare, only 17 cases having been reported in the literature. We report an additional case of this rare disease. The patient was a 5-year-old boy who complained of left exophthalmos and double vision. Computed tomography (CT) and magnetic resonance imaging (MRI) revealed a large epidural mass in the left frontal region that had invaded into the underlying anterior skull base. The tumor showed homogeneous, low density with nonhomogeneous contrast enhancement on the CT scans, and low intensity on the T1-weighted and high intensity on the T2-weighted MRI images. A whitish-pink, elastic, hard tumor was revealed in the epidural space in the left anterior cranial fossa, which was totally excised with curettage of the affected anterior skull base. The origin of the tumor was suspected to be the fibrous connective tissue of the sphenofrontal suture. The histological diagnosis was that of cranial fasciitis. There was no evidence of recurrence 1 year postoperatively.

Child, Preschool↗

PubMed2005

A comparison of morphometric characteristics of sperm from fertile Bos taurus and Bos indicus bulls in Brazil.

The current work has as main objective the systematic investigation of sperm morphometric characteristics of fertile Bos taurus and Bos indicus bulls living in Brazil by using several traditional as well as more modern and advanced computer vision concepts and methodologies. Ten smears of B. taurus semen and ten smears of B. indicus semen have been evaluated. Sperm morphology was quantified in terms of the following morphological features: head area, perimeter, width, length, width:length ratio, ellipticity, shape factor, width of sperm basis, the three first Fourier values, symmetry and hydrodynamics. Morphometric differences have been observed between the sperm cell of B. taurus and B. indicus bulls. The sperm cells of Zebu bulls tend to be smaller and less elliptic, however without modifying hydrodynamic, side symmetry and width of sperm head base. These differences clearly indicate that the geometrical characterization of bull sperm cells should take into account morphological peculiarities that are specific to each subspecies. Another important contribution is the identification that morphological differences implied by bulls of different fertility, as characterized by other authors, were found to be less as compared with those obtained in the current study where highly fertile animals from the two subspecies were studied.

Animals↗

PubMed2005

Case-based object recognition for airborne fungi recognition.

OBJECTIVE: Model-based object recognition is a well-known task in Computer Vision. Usually, one object that can be generalized by a model should be detected in an image based on this model. Biomedical applications have the special quality that one object can have a great variation in appearance. Therefore the appearance of this object cannot be generalized by one model. A set of cases of the appearance of this object (sometimes 50 cases or more) is necessary to detect this object in an image. The recognition method is rather case-based object recognition than model-based object recognition. Case-based object recognition is a challenging task. METHODS AND MATERIAL: It puts special requirements to the similarity measure and needs a matching algorithm that can work fast in a large number of cases. It also needs a case acquisition procedure that can capture the great variation in appearance of an object and generalize these data into a case description. In this paper we describe the chosen case representation, the similarity measure and the matching as well as the case acquisition procedure. We evaluate our method based on a large enough set of digital images containing biological objects such as fungi spores. RESULTS: We can show that the similarity measure is superior to detect the objects in the images. The developed method for case acquisition and learning of generalized cases allows us to learn interactively a sufficient number of cases that are further stored into our case base. Finally, we give results on the performance of the system by calculating the recognition rate. CONCLUSION: These result show that we have developed a novel similarity measure for object detection in digital grey-level images and a novel procedure for case acquisition and learning that allows us to learn a sufficiently large enough case base and to generalize over a group of cases.

Algorithms↗

PubMed2006

Image-type dependent eigen-regions-of-interest define conspicuity operators for predicting human scanpath fixation.

Top-down informativeness elaboration and bottom-up conspicuity processing are intimately interconnected in visual perception. An internal cognitive model of the external world must necessarily control not only our recognition but also the scanpath sequence of eye movement/shift of attention jumps. A self-organizing process based on principal component analysis and scanpath experimental data are used in this paper to define spatial visual conspicuity from the eigen-features of the scanpath sequence image loci. Eight different classes of images are used as both training and testing set. We first demonstrate that cognitive-driven scanpath loci can be discriminated in terms of these bottom-up eigen conspicuity features. We can finally define a conspicuity processing algorithm and measure its ability to predict human scanpaths as evidenced by the positional similarity measure Sp. Some computer vision applications will also be discussed.

Algorithms↗