Purang Abolmaesumi

Image-guided control of a robot for medical ultrasound

A robot-assisted system for medical diagnostic ultrasound has been developed by the authors. The paper presents the visual servo controller used in this system. While the ultrasound transducer is positioned by a robot, the operator, the robot controller, and an ultrasound image processor have shared control over its motion. Ultrasound image features that can be selected by the operator are recognized and tracked by a variety of techniques. Based on feature tracking, ultrasound image servoing in three axes has been incorporated in the interface and can be enabled to automatically compensate, through robot motions, unwanted motions in the plane of the ultrasound beam. The accuracy of the system is illustrated through a 3-D reconstruction of an ultrasound phantom. An Internet-based robot-assisted teleultrasound system has also been demonstrated.

A software implementation of a genetic algorithm based approach to network intrusion detection

With the rapid expansion of Internet in recent years, computer systems are facing increased number of security threats. Despite numerous technological innovations for information assurance, it is still very difficult to protect computer systems. Therefore, unwanted intrusions take place when the actual software systems are running. Different soft computing based approaches have been proposed to detect computer network attacks. This paper presents a genetic algorithm (GA) based approach to network intrusion detection, and the software implementation of the approach. The genetic algorithm is employed to derive a set of classification rules from network audit data, and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify network intrusions in a real-time environment. Unlike most existing GA-based approaches, because of the simple representation of rules and the effective fitness function, the proposed method is easier to implement while providing the flexibility to either generally detect network intrusions or precisely classify the types of attacks. Experimental results show the achievement of acceptable detection rates based on benchmark DARPA data sets on intrusions, while no other complementary techniques or relevant heuristics are applied.

Speckle Noise Reduction of Medical Ultrasound Images in Complex Wavelet Domain Using Mixture Priors

Speckle noise is an inherent nature of ultrasound images, which may have negative effect on image interpretation and diagnostic tasks. In this paper, we propose several multiscale nonlinear thresholding methods for ultrasound speckle suppression. The wavelet coefficients of the logarithm of image are modeled as the sum of a noise-free component plus an independent noise. Assuming that the noise-free component has some local mixture distribution (MD), and the noise is either Gaussian or Rayleigh, we derive the minimum mean squared error (MMSE) and the averaged maximum (AMAP) estimators for noise reduction. We use Gaussian and Laplacian MD for each noise-free wavelet coefficient to characterize their heavy-tailed property. Since we estimate the parameters of the MD using the expectation maximization (EM) algorithm and local neighbors, the proposed MD incorporates some information about the intrascale dependency of the wavelet coefficients. To evaluate our spatially adaptive despeckling methods, we use both real medical ultrasound and synthetically introduced speckle images for speckle suppression. The simulation results show that our method outperforms several recently and the state-of-the-art techniques qualitatively and quantitatively.

High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models.

In this paper we present a high-throughput system for detecting regions of carcinoma of the prostate (CaP) in HSs from radical prostatectomies (RPs) using probabilistic pairwise Markov models (PPMMs), a novel type of Markov random field (MRF). At diagnostic resolution a digitized HS can contain 80Kx70K pixels - far too many for current automated Gleason grading algorithms to process. However, grading can be separated into two distinct steps: (1) detecting cancerous regions and (2) then grading these regions. The detection step does not require diagnostic resolution and can be performed much more quickly. Thus, we introduce a CaP detection system capable of analyzing an entire digitized whole-mount HS (2x1.75cm(2)) in under three minutes (on a desktop computer) while achieving a CaP detection sensitivity and specificity of 0.87 and 0.90, respectively. We obtain this high-throughput by tailoring the system to analyze the HSs at low resolution (8microm per pixel). This motivates the following algorithm: (Step 1) glands are segmented, (Step 2) the segmented glands are classified as malignant or benign, and (Step 3) the malignant glands are consolidated into continuous regions. The classification of individual glands leverages two features: gland size and the tendency for proximate glands to share the same class. The latter feature describes a spatial dependency which we model using a Markov prior. Typically, Markov priors are expressed as the product of potential functions. Unfortunately, potential functions are mathematical abstractions, and constructing priors through their selection becomes an ad hoc procedure, resulting in simplistic models such as the Potts. Addressing this problem, we introduce PPMMs which formulate priors in terms of probability density functions, allowing the creation of more sophisticated models. To demonstrate the efficacy of our CaP detection system and assess the advantages of using a PPMM prior instead of the Potts, we alternately incorporate both priors into our algorithm and rigorously evaluate system performance, extracting statistics from over 6000 simulations run across 40 RP specimens. Perhaps the most indicative result is as follows: at a CaP sensitivity of 0.87 the accompanying false positive rates of the system when alternately employing the PPMM and Potts priors are 0.10 and 0.20, respectively.

An interacting multiple model probabilistic data association filter for cavity boundary extraction from ultrasound images

This paper presents a novel segmentation technique for extracting cavity contours from ultrasound images. The problem is first discretized by projecting equispaced radii from an arbitrary seed point inside the cavity toward its boundary. The distance of the cavity boundary from the seed point is modeled by the trajectory of a moving object. The motion of this moving object is assumed to be governed by a finite set of dynamical models subject to uncertainty. Candidate edge points obtained along each radius include the measurement of the object position and some false returns. The modeling approach enables us to use the interacting multiple model estimator along with a probabilistic data association filter, for contour extraction. The convergence rate of the method is very fast because it does not employ any numerical optimization. The robustness and accuracy of the method are demonstrated by segmenting contours from a series of ultrasound images. The results are validated through comparison with manual segmentations performed by an expert. An application of the method in segmenting bone contours from computed tomography images is also presented.

Point-Based Rigid-Body Registration Using an Unscented Kalman Filter

We present and validate a novel registration algorithm mapping two data sets, generated from a rigid object, in the presence of Gaussian noise. The proposed method is based on the unscented Kalman filter (UKF) algorithm that is generally employed for analyzing nonlinear systems corrupted by additive Gaussian noise. First, we employ our proposed registration algorithm to fit two randomly generated data sets in the presence of isotropic Gaussian noise, when the corresponding points between the two data sets are assumed to be known. Then, we extend the registration method to the case where the data (with known correspondences) is stimulated by anisotropic Gaussian noise. The new registration method not only reliably converges to the correct registration solution, but it also estimates the variance, as a confidence measure, for each of the estimated registration transformation parameters. Furthermore, we employ the proposed registration algorithm for rigid-body, point-based registration where corresponding points between two registering data sets are unknown. The algorithm is tested on point data sets which are garnered from a pelvic cadaver and a scaphoid bone phantom by means of computed tomography (CT) and tracked free-hand ultrasound imaging. The collected 3-D points in the ultrasound frame are registered to the 3-D meshes in the CT frame by using the proposed and the standard iterative closest points (ICP) registration algorithms. Experimental results demonstrate that our proposed method significantly outperforms the ICP registration algorithm in the presence of additive Gaussian noise. It is also shown that the proposed registration algorithm is more robust than the ICP registration algorithm in terms of outliers in data sets and initial misalignment between the two data sets.

A Real-Time Freehand Ultrasound Calibration System with Automatic Accuracy Feedback and Control

This article describes a fully automatic, real-time, freehand ultrasound calibration system. The system was designed to be simple and sterilizable, intended for operating-room usage. The calibration system employed an automatic-error-retrieval and accuracy-control mechanism based on a set of ground-truth data. Extensive validations were conducted on a data set of 10,000 images in 50 independent calibration trials to thoroughly investigate the accuracy, robustness, and performance of the calibration system. On average, the calibration accuracy (measured in three-dimensional reconstruction error against a known ground truth) of all 50 trials was 0.66 mm. In addition, the calibration errors converged to submillimeter in 98% of all trials within 12.5 s on average. Overall, the calibration system was able to consistently, efficiently and robustly achieve high calibration accuracy with real-time performance.

Lumbar Spine Segmentation Using a Statistical Multi-Vertebrae Anatomical Shape+Pose Model

Segmentation of the spinal column from computed tomography (CT) images is a preprocessing step for a range of image-guided interventions. One intervention that would benefit from accurate segmentation is spinal needle injection. Previous spinal segmentation techniques have primarily focused on identification and separate segmentation of each vertebra. Recently, statistical multi-object shape models have been introduced to extract common statistical characteristics between several anatomies. These models can be used for segmentation purposes because they are robust, accurate, and computationally tractable. In this paper, we develop a statistical multi-vertebrae shape+pose model and propose a novel registration-based technique to segment the CT images of spine. The multi-vertebrae statistical model captures the variations in shape and pose simultaneously, which reduces the number of registration parameters. We validate our technique in terms of accuracy and robustness of multi-vertebrae segmentation of CT images acquired from lumbar vertebrae of 32 subjects. The mean error of the proposed technique is below 2 mm, which is sufficient for many spinal needle injection procedures, such as facet joint injections.

Real-time extraction of carotid artery contours from ultrasound images

This paper presents the development of a novel, fully-automatic tracking and segmentation system to extract the boundary of the carotid artery from ultrasound images in real-time. The center of the carotid artery is tracked using the Star algorithm. The stability of the Star algorithm has been improved by using a temporal Kalman filter. A spatial Kalman filter is used to estimate the carotid artery boundary. Since the method does not employ any numerical optimization, convergence is very fast. The stability and accuracy of the method is demonstrated by tracking the carotid artery over a 30 second sequence of ultrasound images taken during a carotid artery examination. An application of the tracking method to ultrasound image servoing is also presented.

A Robot System for Medical Ultrasound

A teleoperation approach to diagnostic ultrasound, in which the ultrasound transducer is positioned by a robot, is described in this paper. An inherently safe counterbalanced robot has been designed and tested in carotid artery examinations. The feasibility of using visual servoing for motion in the plane of the ultrasound probe has also been demonstrated using a modified image correlation algorithm and feature tracking algorithms. Research issues that have arisen in developing this and other systems designed for human augmentation are also presented.