Showing papers by "Dorin Comaniciu published in 2007"

Fast Automatic Heart Chamber Segmentation from 3D CT Data Using Marginal Space Learning and Steerable Features

Abstract: 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.

Shape regression machine

Abstract: We present a machine learning approach called shape regression machine (SRM) to segmenting in real time an anatomic structure that manifests a deformable shape in a medical image. Traditional shape segmentation methods rely on various assumptions. For instance, the deformable model assumes that edge defines the shape; the Mumford-Shah variational method assumes that the regions inside/outside the (closed) contour are homogenous in intensity; and the active appearance model assumes that shape/appearance variations are linear. In addition, they all need a good initialization. In contrast, SRM poses no such restrictions. It is a two-stage approach that leverages (a) the underlying medical context that defines the anatomic structure and (b) an annotated database that exemplifies the shape and appearance variations of the anatomy. In the first stage, it solves the initialization problem as object detection and derives a regression solution that needs just one scan in principle. In the second stage, it learns a nonlinear regressor that predicts the nonrigid shape from image appearance. We also propose a boosting regression approach that supports real time segmentation. We demonstrate the effectiveness of SRM using experiments on segmenting the left ventricle endocardium from an echocardiogram of an apical four chamber view.

System and method for segmenting chambers of a heart in a three dimensional image

Abstract: A system and method for segmenting chambers of a heart in three dimensional images is disclosed. A set of three dimensional images of a heart is received. The shape of the heart in the three dimensional images is localized. Boundaries of the chambers of the heart in the localized shape are identified using steerable features.

System and Method For Detecting and Tracking A Guidewire In A Fluoroscopic Image Sequence

Abstract: A system and method for populating a database with a set of image sequences of an object is disclosed. The database is used to detect localization of a guidewire in the object. A set of images of anatomical structures is received in which each image is annotated to show a guidewire, catheter, wire tip and stent. For each given image a Probabilistic Boosting Tree (PBT) is used to detect short line segments of constant length in the image. Two segment curves are constructed from the short line segments. A discriminative joint shape and appearance model is used to classify each two segment curve. A shape of an n-segment curve is constructed by concatenating all the two segment curves. A guidewire curve model is identified that includes a start point, end point and the n-segment curve. The guidewire curve model is stored in the database.

Hierarchical Learning of Curves Application to Guidewire Localization in Fluoroscopy

Abstract: 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.

Automatic Cardiac View Classification of Echocardiogram

Abstract: We propose a fully automatic system for cardiac view classification of echocardiogram. Given an echo study video sequence, the system outputs a view label among the pre-defined standard views. The system is built based on a machine learning approach that extracts knowledge from an annotated database. It characterizes three features: 1) integrating local and global evidence, 2) utilizing view specific knowledge, and 3) employing a multi-class Logit-boost algorithm. In our prototype system, we classify four standard cardiac views: apical four chamber and apical two chamber, parasternal long axis and parasternal short axis (at mid cavity). We achieve a classification accuracy over 96% both of training and test data sets and the system runs in a second in the environment of Pentium 4 PC with 3.4 GHz CPU and 1.5 G RAM.

A boosting regression approach to medical anatomy detection

Abstract: The state-of-the-art object detection algorithm learns a binary classifier to differentiate the foreground object from the background. Since the detection algorithm exhaustively scans the input image for object instances by testing the classifier, its computational complexity linearly depends on the image size and, if say orientation and scale are scanned, the number of configurations in orientation and scale. We argue that exhaustive scanning is unnecessary when detecting medical anatomy because a medical image offers strong contextual information. We then present an approach to effectively leveraging the medical context, leading to a solution that needs only one scan in theory or several sparse scans in practice and only one integral image even when the rotation is considered. The core is to learn a regression function, based on an annotated database, that maps the appearance observed in a scan window to a displacement vector, which measures the difference between the configuration being scanned and that of the target object. To achieve the learning task, we propose an image-based boosting ridge regression algorithm, which exhibits good generalization capability and training efficiency. Coupled with a binary classifier as a confidence scorer, the regression approach becomes an effective tool for detecting left ventricle in echocardiogram, achieving improved accuracy over the state-of-the-art object detection algorithm with significantly less computation.

Joint Real-time Object Detection and Pose Estimation Using Probabilistic Boosting Network

Abstract: In this paper, we present a learning procedure called probabilistic boosting network (PBN) for joint real-time object detection and pose estimation. Grounded on the law of total probability, PBN integrates evidence from two building blocks, namely a multiclass boosting classifier for pose estimation and a boosted detection cascade for object detection. By inferring the pose parameter, we avoid the exhaustive scanning for the pose, which hampers real time requirement. In addition, we only need one integral image/volume with no need of image/volume rotation. We implement PBN using a graph-structured network that alternates the two tasks of foreground/background discrimination and pose estimation for rejecting negatives as quickly as possible. Compared with previous approaches, we gain accuracy in object localization and pose estimation while noticeably reducing the computation. We invoke PBN to detect the left ventricle from a 3D ultrasound volume, processing about 10 volumes per second, and the left atrium from 2D images in real time.

Scalable Semantic Image Search

Abstract: A computer-implemented system for searching a plurality of images for an image of interest including a database of semantic image representations corresponding to the plurality of images, wherein the semantic image representations link a semantic model of clinical properties, a syntactic model of high level image properties and an image vocabulary of low level image properties, a set of queries associated with the semantic image representations, and a semantic search engine, embodied as computer readable code executed by a processor, for receiving a search query, selecting at least one of the set of queries based on the search query, and searching the plurality of images for the image of interest by comparing the plurality of images against the semantic image representations associated with a selected query.

Method and System For Regression-Based Object Detection in Medical Images

Abstract: A method and system for regression-based object detection in medical images is disclosed. A regression function for predicting a location of an object in a medical image based on an image patch is trained using image-based boosting ridge regression (IBRR). The trained regression function is used to determine a difference vector based on an image patch of a medical image. The difference vector represents the difference between the location of the image patch and the location of a target object. The location of the target object in the medical image is predicted based on the difference vector determined by the regression function.

Systems and Method For Integrative Medical Decision Support

Abstract: A system for providing medical decision support for diagnosis and treatment of disease comprises a medical knowledge database comprising medical information, the medical information including probabilities of disease outcomes for a disease of interest, a memory device for storing a program, a processor in communication with the memory device, the processor operative with the program to obtain patient information and in vitro test results for a patient, and automatically generate a recommendation for a medical test based on a combination of the patient information, the in vitro test results, and medical information from the medical knowledge database.

A probabilistic, hierarchical, and discriminant framework for rapid and accurate detection of deformable anatomic structure

Abstract: We propose a probabilistic, hierarchical, and discriminant (PHD) framework for fast and accurate detection of deformable anatomic structures from medical images. The PHD framework has three characteristics. First, it integrates distinctive primitives of the anatomic structures at global, segmental, and landmark levels in a probabilistic manner. Second, since the configuration of the anatomic structures lies in a high-dimensional parameter space, it seeks the best configuration via a hierarchical evaluation of the detection probability that quickly prunes the search space. Finally, to separate the primitive from the background, it adopts a discriminative boosting learning implementation. We apply the PHD framework for accurately detecting various deformable anatomic structures from M- mode and Doppler echocardiograms in about a second.

Automatic fetal measurements in ultrasound using constrained probabilistic boosting tree

Abstract: Automatic delineation and robust measurement of fetal anat-omical structures in 2D ultrasound images is a challenging task due to the complexity of the object appearance, noise, shadows, and quantity of information to be processed. Previous solutions rely on explicit encoding of prior knowledge and formulate the problem as a perceptual grouping task solved through clustering or variational approaches. These methods are known to be limited by the validity of the underlying assumptions and cannot capture complex structure appearances. We propose a novel system for fast automatic obstetric measurements by directly exploiting a large database of expert annotated fetal anatomical structures in ultrasound images. Our method learns to distinguish between the appearance of the object of interest and background by training a discriminative constrained probabilistic boosting tree classifier. This system is able to handle previously unsolved problems in this domain, such as the effective segmentation of fetal abdomens. We show results on fully automatic measurement of head circumference, biparietal diameter, abdominal circumference and femur length. Unparalleled extensive experiments 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.

Computer Aided Detection and Decision Support

Abstract: A system (100) for computer aided detection and decision support includes an ontology of image representations (101) for injecting meaning into and adding relationships among image contents, an image understanding and parsing module (103) in communication with the ontology of image representations (101) for extracting structures from an image (104) including the image contents, and a reasoning engine (102) in communication with the ontology and the image understanding and parsing module (103) for classifying the image contents, wherein the system (100) receives the image (104) and corresponding descriptive information (105).

System and method for detecting an object in a high dimensional space

Abstract: A system and method for detecting an object in a high dimensional image space is disclosed. A three dimensional image of an object is received. A first classifier is trained in the marginal space of the object center location which generates a predetermined number of candidate object center locations. A second classifier is trained to identify potential object center locations and orientations from the predetermined number of candidate object center locations and maintaining a subset of the candidate object center locations. A third classifier is trained to identify potential locations, orientations and scale of the object center from the subset of the candidate object center locations. A single candidate object pose for the object is identified.

Method and System for Object Detection Using Probabilistic Boosting Cascade Tree

Abstract: A method and system for object detection using a probabilistic boosting cascade tree (PBCT) is disclosed. A PBCT is a machine learning based classifier having a structure that is driven by training data and determined during the training process without user input. In a PBCT training method, for each node in the PBCT, a classifier is trained for the node based on training data received at the node. The performance of the classifier trained for the node is then evaluated based on the training data. Based on the performance of the classifier, the node is set to either a cascade node or a tree node. If the performance indicates that the data is relatively easy to classify, the node can be set as a cascade node. If the performance indicates that the data is relatively difficult to classify, the node can be set as a tree node. The trained PBCT can then be used to detect objects or classify data. For example, a trained PBCT can be used to detect lymph nodes in CT volume data.

System and method for online optimization of guidewire visibility in fluoroscopic systems

Abstract: A method for online optimization of guidewire visibility in fluoroscopic images includes providing an digitized image acquired from a fluoroscopic imaging system, the image comprising an array of intensities corresponding to a 2-dimensional grid of pixels, detecting a guidewire in the fluoroscopic image, enhancing the visibility of the guidewire in the fluoroscopic image, calculating a visibility measure of the guidewire in the fluoroscopic image, and readjusting acquisition parameters of the fluoroscopic imaging system wherein the guidewire visibility is improved.

System and method for simultaneously subsampling fluoroscopic images and enhancing guidewire visibility

Abstract: A method for downsampling fluoroscopic images and enhancing guidewire visibility during coronary angioplasty includes providing a first digitized image, filtering the image with one or more steerable filters of different angular orientations, assigning a weight W and orientation O for each pixel based on the filter response for each pixel, wherein each pixel weight is assigned to a function of a maximum filter response magnitude and the pixel orientation is calculated from the angle producing the maximum filter response if the magnitude is greater than zero, wherein guidewire pixels have a higher weight than non-guidewire pixels, and downsampling the orientation and weights to calculate a second image of half the resolution of the first image, wherein the downsampling accounts for the orientation and higher weight assigned to the guidewire pixels.

System and method for detecting a three dimensional flexible tube in an object

Abstract: The present invention is directed to a system and method for populating a database with a set of image sequences of an object. The database is used to detect a tubular structure in the object. A set of images of objects are received in which each image is annotated to show a tubular structure. For each given image, a Probabilistic Boosting Tree (PBT) is used to detect three dimensional (3D) circles. Short tubes are constructed from pairs of approximately aligned 3D circles. A discriminative joint shape and appearance model is used to classify each short tube. A long flexible tube is formed by connecting all of the short tubes. A tubular structure model that comprises a start point, end point and the long flexible tube is identified. The tubular structure model is stored in the database.

Fast detection of left ventricle and its configuration in 2D/3D echocardiogram using probabilistic boosting network

Abstract: A method for detecting an object of interest in an input image includes the computer-implemented steps of: receiving an image, providing a multi-class pose classifier that identifies a plurality of pose features for estimating a pose of the object of interest, providing a plurality of cascades of serially-linked binary object feature classifiers, each cascade corresponding to different poses of the object of interest in the input image, selecting at least one of the cascades using the estimated pose, and employing the selected cascades to detect instances of the object of interest in the image.

System und Verfahren zum Segmentieren von Kammern eines Herzens in einem dreidimensionalen Bild

Abstract: Ein System und ein Verfahren zum Segmentieren von Kammern eines Herzens in dreidimensionalen Bildern wird offenbart. Ein Satz von dreidimensionalen Bildern eines Herzens wird empfangen. Die Form des Herzens wird in den dreidimensionalen Bildern lokalisiert. Grenzen der Kammern des Herzens in der lokalisierten Form werden identifiziert unter Verwendung von steuerbaren Merkmalen.

Component-Based Approach For Fast Left Ventricle Detection

Abstract: A method for estimating a configuration of an internal structure within a medical image includes detecting a location of the internal structure. Component-based identification is performed within the detected location of the internal structure to identify a plurality of components. The configuration of the internal structure is estimated based on the relative position of the identified components.