Contour segmentation in 2D ultrasound medical images with particle filtering

Object segmentation in medical images is an actively investigated research area. Segmentation techniques are a valuable tool in medical diagnostics for cancer tumours and cysts, for planning surgery operations and other medical treatment. In this paper, a Monte Carlo algorithm for extracting lesion contours in ultrasound medical images is proposed. An efficient multiple model particle filter for progressive contour growing (tracking) from a starting point is developed, accounting for convex, non-circular forms of delineated contour areas. The driving idea of the proposed particle filter consists in the incorporation of different image intensity inside and outside the contour into the filter likelihood function. The filter employs image intensity gradients as measurements and requires information about four manually selected points: a seed point, a starting point, arbitrarily selected on the contour, and two additional points, bounding the measurement formation area around the contour. The filter performance is studied by segmenting contours from a number of real and simulated ultrasound medical images. Accurate contour segmentation is achieved with the proposed approach in ultrasound images with a high level of speckle noise.     

Contour tracking with automatic motion model switching

In this paper we present an efficient contour-tracking algorithm which can track 2D silhouette of objects in extended image sequences. We demonstrate the ability of the tracker by tracking highly deformable contours (such as walking people) captured by a static camera. We represent contours (silhouette) of moving objects by using a cubic B-spline. The tracking algorithm is based on tracking a lower dimensional shape space (as opposed to tracking in spline space). Tracking the lower dimensional space has proved to be fast and efficient. The tracker is also coupled with an automatic motion-model switching algorithm, which makes the tracker robust and reliable when the object of interest is moving with multiple motion. The model-based tracking technique provided is capable of tracking rigid and non-rigid object contours with good tracking accuracy.     

Contour tracing of grey-scale images based on 2-D histograms

This paper describes two methods for tracing the contour line of a digitized grey-scale object by following the estimated contour path directly on the grey-scale image. This search is performed by classifying the potential next contour points as belonging or not belonging to the contour. Experimental results are reported, showing the satisfactory performance and the advantages of both methods.     

Spatiotemporal filtering of sequences of ultrasound images to estimate a dense field of velocities

Blood velocity estimation is required in many clinical applications. Ultrasonic imaging is often used to reach this goal. This article presents a velocity vector estimation method from ultrasonic imaging. It complements Doppler imaging, which has several limitations. New techniques such as block-matching (BM) and decorrelation-based methods have already been developed to overcome these limitations. Our method is based on spatiotemporal filtering to estimate the apparent velocity vector for each pixel of the sequence of ultrasound images. A moving object is represented by a group of pixels travelling from image to image in the sequence, leaving a trace in the spatiotemporal volume. A bank of filters was designed to estimate a local texture orientation related to the velocity of the object. The method was first developed in 2D then extended in 3D to estimate the two components in the imaging plane. The method was applied to sequences of ultrasound images of calibrated flow in a vessel (mean velocity ). The velocity estimates obtained in 2D and 3D showed mean errors less than 5% and 12%, respectively. The results are presented as dynamic cartography and dense fields of velocity vectors. The associated velocity profiles show good agreement with the theoretical parabolic profile of laminar flow. Our approach has been compared with three other velocity estimation methods and showed good performance in comparison with them.     

Geometric techniques for 3D tracking of ultrasound sensor,tumor segmentation in ultrasound images,and 3D reconstruction

This paper presents different methods, some based on geometric algebra, for ultrasound probe tracking in endoscopic images, 3D allocation of the ultrasound probe, ultrasound image segmentation (to extract objects like tumors), and 3D reconstruction of the surface defined by a set of points. The tracking of the ultrasound probe in endoscopic images is done with a particle filter and an auxiliary method based on thresholding in the HSV space. The 3D pose of the ultrasound probe is calculated using conformal geometric algebra (to locate each slide in 3D space). Each slide (ultrasound image) is segmented using two methods: the level-set method and the morphological operators approach in order to obtain the object we are interested in. The points on the object of interest are obtained from the segmented ultrasound images, and then a 3D object is obtained by refining the convex hull. To do that, a peeling process with an adaptive radius is applied, all of this in the geometric algebra framework. Results for points from ultrasound images, as well as for points from objects from the AimatShape Project, are presented (A.I.M.A.T.S.H.A.P.E. – Advanced an Innovative Models And Tools for the development of Semantic-based systems for Handling, Acquiring, and Processing knowledge Embedded in multidimensional digital objects).     

Contour analysis-based matching of ground objects in aerial images

An original approach to matching images is proposed based on the technique of comparison of the structure of contours. The technique for detecting contours is reinforced by combining the use of data from independent contours and contours that are the boundaries of regions. For this purpose, several assumptions and simple rules of general nature are formulated. Contours are identified based on local salient features of their one-dimensional representation. To avoid ambiguity in identifying contours, the technique of dynamic programming is harnessed. All computational procedures are invariant to image rotation. In conclusion the developed method is applied to the problem of matching image fragments in aerial images.     

Contour graph based human tracking and action sequence recognition

This paper introduces a new framework for human contour tracking and action sequence recognition. Given a gallery of labeled human contour sequences, we define each contour as a “word” and encode all of them into a contour dictionary. This dictionary will be used to translate the video. To this end, a contour graph is constructed by connecting all the neighboring contours. Then, the motion in a video is viewed as an instance of random walks on this graph. As a result, we can avoid explicitly parameterizing the contour curves and modeling the dynamical system for contour updating. In such a work setting, there are only a few state variables to be estimated when using sequence Monte Carlo (SMC) approach to realize the random walks. In addition, the walks on the graph also perform sequence comparisons implicitly with those in the predefined gallery, from which statistics about class label is evaluated for action recognition. Experiments on diving tracking and recognition illustrate the validity of our method.     

Contour tracking and corner detection in a logic programmingenvironment

The added functionality such as contour tracking and corner detection which logic programming lends to standard image operators is described. An environment for implementing low-level imaging operations with Prolog predicates is considered. Within this environment, higher-level image predicates (contour tracking and corner detection) are constructed. The emphasis is not on building better corner detectors, but on presenting ways of using the unification and backtracking features of logic programming for these tasks. The performance of this implementation of contour tracking and corner detection has been very good in many more complex images, as it allows for feedback both ways between sensor input and symbolic models. More important is the parameter selection capability in a dynamic version where background properties change. The authors present examples of Prolog predicates for performing the contour and corner detection operations     

Prediction and tracking of long-range-dependent sequences

Long-range-dependent (LRD) sequences have been found to be of importance in various fields such as telecommunications, signal processing and finance. Since the history of an LRD sequence has significant impact on the present values, it is expected that accurate prediction and tracking of these sequences are easier than of short-range-dependent sequences. The purpose of this paper is to verify whether distant observations in the past might increase the performance of a constrained tracker significantly when this information from the past is used in combination with recent observations.     

Feature point tracking in time-varying images

This paper examines the problem of tracking feature points in dynamic imagery. Two types of imaging geometries are examined: orthographic projection and perspective projection. In each case, the goal is to perform the feature point tracking with a minimal number of images. The feature point tracking problem is shown to be intractable and a grddey heuristic is presented.     

Segmentation and tracking of piglets in images

An algorithm was developed for the segmentation and tracking of piglets and tested on a 200-image sequence of 10 piglets moving on a straw background. The image-capture rate was 1 image/140 ms. The segmentation method was a combination of image differencing with respect to a median background and a Laplacian operator. The features tracked were blob edges in the segmented image. During tracking, the piglets were modelled as ellipses initialised on the blobs. Each piglet was tracked by searching for blob edges in an elliptical window about the piglet's position, which was predicted from its previous two positions.     

Adaptive tracking of multiple hot-spot target IR images

In the recent past, the capability of tracking dynamic targets from forward-looking infrared (FLIR) measurements has been improved substantially, by replacing standard correlation trackers with adaptive extended Kalman filters. This research investigates a tracker able to handle "multiple hot-spot" targets, in which digital (or optical) signal processing is employed on the FLIR data to identify the underlying target shape. This identified shape is then used in the measurement model portion of the filter as it estimates target offset from the center of the field-of-view. In this algorithm, an extended Kalman filter processes the raw intensity measurements from the FLIR to produce target estimates. An alternative algorithm uses a linear Kalman filter to process the position indications of an enhanced correlator in order to generate tracking estimates; the enhancement is accomplished not only by thresholding to eliminate poor correlation information, but also by incorporating the dynamics information from the Kalman filter and the on-line identification of the target shape as a template instead of merely using previous frames of data. The performance capabilities of these two algorithms are evaluated under various tracking environment conditions and for a range of choices of design parameters.     

Digital processing of sequences of multizonal images

A solution to the problem of detecting extended anomalies in a sequence of multizonal images is suggested. New optimal detection algorithms are synthesized. Analytic relations for analyzing their performance are found. Konstantin K. Vasil’ev, born 1948. Graduated from Leningrad Electrotechnical Institute in 1972. Received Candidate’s degree in 1975 and Doctoral degree in 1985. Honored Science and Technology Worker of Russian Federation, Corresponding member of the Academy of Sciences of Republic Tatarstan, member of the Presidium of Central Council of the Popov Russian Scientific-Engineering Society of Radioengineering, Electronics, and Communications, Chairman of the Telecommunication Department of Ul’yanovsk State Technical University. In 2007, awarded a medal for enhancing the prestige of Russian science. Scientific interests: modeling and statistical analysis of random processes and fields.     

足球视频中的目标检测与跟踪

提出了一种新颖的目标检测与跟踪算法来检测和跟踪足球运动中的球员。与普通的利用颜色分割的方法不同,考虑到足球视频中非目标的像素大体上都是单一的绿色这个特点,结合颜色的统计信息和像素的边缘特性来得到更完美的检测效果;接着利用灰度图像中的统计信息,轻松地完成球员队属的辨别;最后根据重叠面积提出一种简单的视频目标跟踪方案,结合图像匹配,解决运动中的遮挡问题。     

Parallel border tracking in binary images using GPUs

The Journal of Supercomputing - Border tracking in binary images is an important kernel for many applications. There are very efficient sequential algorithms, most notably, the algorithm proposed...     

Speckle reduction of ultrasound images based on Rayleigh-trimmed anisotropic diffusion filter

A novel method is proposed to reduce speckle in ultrasound images. Based on the assumption of Rayleigh distribution of speckle, a Rayleigh-trimmed filter is first proposed to estimate the relative standard deviations of local signals and the results are used to determine the parameter that controls an alpha-trimmed mean filter for suppressing the primary noise. Then the anisotropic diffusion is subsequently applied to further reduce noise while enhancing features and structures in the original image. We also extend the proposed method to three-dimensional space by introducing time as one additional dimension. The proposed method effectively utilizes the statistical characteristics of speckle and the two-step despeckling algorithm reduces speckle significantly while retaining important features. The effectiveness of the proposed method is well demonstrated by experiments on both simulated and real ultrasound images.     

Object tracking in image sequences using point features

This paper presents an object tracking technique based on the Bayesian multiple hypothesis tracking (MHT) approach. Two algorithms, both based on the MHT technique are combined to generate an object tracker. The first MHT algorithm is employed for contour segmentation. The segmentation of contours is based on an edge map. The segmented contours are then merged to form recognisable objects. The second MHT algorithm is used in the temporal tracking of a selected object from the initial frame. An object is represented by key feature points that are extracted from it. The key points (mostly corner points) are detected using information obtained from the edge map. These key points are then tracked through the sequence. To confirm the correctness of the tracked key points, the location of the key points on the trajectory are verified against the segmented object identified in each frame. If an acceptable number of key-points lie on or near the contour of the object in a particular frame (n-th frame), we conclude that the selected object has been tracked (identified) successfully in frame n.