elysium bio-medical 2010

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Bio - Medical 2010 - 2011

01 3D CORONARY STRUCTURE TRACKING ALGORITHM ITH REGULARIZATION AND MULTIPLE HYPOTHESES IN MRI

This paper presents an improved version of a tracking algorithm for the extraction of the 3D central axis of tubular-like object in a low-contrast

and multi-object environment. This improvement concerns two aspects: (1) an efficient Infinite Impulse Response (IIR) filtering of the

successive tracking directions is used to introduce regularization and (2) a multiple hypotheses testing procedure allows an almost exhaustive

selection of the best centerline location by building different paths according to the applied parameters. A score is then computed for each

hypothesis that helps in the selection of the best path along the tree. The improved tracking algorithm is tested on Magnetic Resonance

Angiography (MRA) datasets to extract the coronary artery centerlines

02 A Magnetic Retrieval System for Stents in the Pancreaticobiliary Tree

Clinical endoscopic intervention of the pancreaticobiliary tree [endoscopic retrograde cholangiopancreatography (ERCP)] often concludes

with the insertion of a temporary plastic stent to reduce the risk of post-ERCP complications by promoting continued flow of bile and

pancreatic fluids. This stent is later removed once the patient has fully recovered, but today this necessitates a second endoscopic

intervention. The final goal of this work is to obviate the second intervention. This is to be achieved by adding a magnetic ring to the stent

such that the stent is removed using a hand-held magnet, held in a suitable position ex vivo. This paper details the design, optimization, and

both ex vivo and in vivo testing of the magnetized stent and hand-held magnet, which has been accomplished to date. The optimized design

for the Hand-held magnet and the modified stent with a magnetic attachment perform in line with simulated expectations, and successful

retrieval is achieved in the porcine ex vivo setting at 9–10 cm separation. This is comparable to the mean target capture distance of 10 cm

between the entry point to the biliary system and the closest cutaneous surface, determined from random review of clinical fluoroscopies in

ten human patients. Subsequently, the system was successfully tested in vivo in the acute porcine model, where retrieval at an estimated

separation of 5–6 cm was captured on endoscopic video. These initial results indicate that the system may represent a promising approach for

the elimination of a second endoscopic procedures following placement of pancreatic and biliary stents

03 Tracking Endocardial Motion via Multiple Model Filtering

Tracking heart motion plays an essential role in the diagnosis of cardiovascular diseases. As such, accurate characterization of dynamic

behavior of the left ventricle (LV) is essential in order to enhance the performance of motion estimation. However, a single Markovian model is

not sufficient due to the substantial variability in typical heart motion. Moreover, dynamics of an abnormal heart could be very different from

that of a normal heart. This study introduces a tracking approach based on multiplemodels, each matched to a different phase of the LV

motion. First, the algorithm adopts a graph cut distribution matching method to tackle the problem of segmenting LV cavity from cardiac MR

images, which is acknowledged as a difficult problem because of low contrast and photometric similarities between the heart wall and

papillary muscles within the LV cavity. Second, interacting multiple models (IMM), an effective estimation algorithm forMarkovian switching

system, is devised subsequent to the segmentations to yield state estimates of the endocardial boundary points. The IMM also yields the

model probability indicating the model that most closely matches the LV motion. The proposed method is evaluated quantitatively by

comparison with independent manual segmentations over 2280 images acquired from 20 subjects, which demonstrated competitive results in

comparisons with related recent methods.





04 Testing Frequency-Domain Causality in Multivariate Time Series

We introduce a new hypothesis-testing framework, based on surrogate data generation, to assess in the frequency domain, the concept of

causality among multivariate (MV) time series. The approach extends the traditional Fourier transform (FT) method for generating surrogate

data in a MV process and adapts it to the specific issue of causality. It generates causal FT (CFT) surrogates with FT modulus taken from the

original series, and FT phase taken from a set of series with causal interactions set to zero over the direction of interest and preserved over all

other directions. Two different zero-setting procedures, acting on the parameters of aMV autoregressive (MVAR) model fitted on the original

series, were used to test the null hypotheses of absence of direct causal influence (CFTd surrogates) and of full (direct and indirect) causal

influence (CFTf surrogates), respectively. CFTf and CFTd surrogates were utilized in combination with the directed coherence (DC) and the

partial DC (PDC) spectral causality estimators, respectively. Simulations reproducing different causality patterns in linear MVAR processes

demonstrated the better accuracy of CFTf and CFTd surrogates with respect to traditional FT surrogates. Application on real MV biological

data measured from healthy humans, i.e., heart period, arterial pressure, and respiration variability, as well as multichannel EEG signals,

showed that CFT surrogates disclose causal patterns in accordance with expected cardio respiratory and neurophysiologic mechanisms.

05 Single-Camera Focus-Based Localization of Intraocular Devices

Future retinal therapies will be partially automated in order to increase the positioning accuracy of surgical tools. Proposed untethered micro

robotic approaches that achieve this increased accuracy require localization information for their control. Since the environment of the human

eye is externally observable, images can be used to localize the micro robots. In this paper, the common methods of ophthalmoscopy

assuming a single stationary camera are examined and compared with respect to their imaging and localizing properties on a schematic model

of the human eye. The first algorithm for wide-angle intraocular localization based on indirect ophthalmoscopy is presented, and its sensitivity

with respect to uncertainties in the parameters of individual eyes is estimated. A calibration technique to account for these uncertainties is

proposed, and the localization algorithm is validated with experiments in a model eye.

06 OI and fMRI Signal Separation Using Both Temporal and Spatial Autocorrelations

Separating brain imaging signals by maximizing their autocorrelations is an important component of blind source separation (BSS). Canonical

correlation analysis (CCA), one of leading BSS techniques, has been widely used for analyzing optical imaging (OI) and functional magnetic resonance

imaging (fMRI) data. However, because of the need to reduce dimensionality and ignore spatial autocorrelation, CCA is problematic for separating

temporal signal sources. To solve the problems of CCA, “straightforward image projection” (SIP) has been incorporated into temporal BSS. This novel

method, termed low-dimensional canonical correlation analysis (LD-CCA), relies on the spatial and temporal autocorrelations of all genuine signals of

interest. Incorporating both spatial and temporal information, here we introduce a “generalized time course” technique in which data are artificially

reorganized prior to separation. The quantity of spatial plus temporal autocorrelations can then be defined. By maximizing temporal and spatial

autocorrelations in combination, LD-CCA is able to obtain expected “real” signal sources. Generalized time courses are low-dimensional, eliminating

the need for dimension reduction. This removes the risk of discarding useful information. The new method is compared with temporal CCA and

temporal independent component analysis (tICA). Comparison of simulated data showed that LD-CCA was more effective for recovering signal

sources. Comparisons using real intrinsic OI and fMRI data also supported the validity of LD-CCA





07 Numerical Model for Estimating RF-Induced Heating on a Pacemaker Implant during MRI: Experimental Validation

MRI may cause tissue heating in patients implanted with pacemakers (PMs) or cardioverters/defibrillators. As a consequence, these patients

are often preventatively excluded from MRI investigations. The issue has been studied for several years now, in order to identify the

mechanisms involved in heat generation, and define safety conditions by which MRI may be extended to patients with active implants. In this

sense, numerical studies not only widen the range of experimental measurements, but also model a realistic patient’s anatomy on which it is

possible to study individually the impact of the many parameters involved. In order to obtain reliable results, however, each and every

numerical analysis needs to be validated by experimental evidence. Aim of this paper was to design and validate through experimental

measurements, an accurate numerical model, which was able to reproduce the thermal effects induced by a birdcage coil on human tissues

containing ametal implant, specifically, a PM. The model was then used to compare the right versus left pectoral implantation of a PM, in terms

of power deposited at the lead tip. This numerical model may also be used as reference for validating simpler models in terms of

computational effort.

08 Nonlinear Features for Single-Channel Diagnosis of Sleep-Disordered Breathing Diseases

Studies have shown that algorithms based on single channel airflow records are effective in screening for sleep disordered breathing diseases

(SDB). In this study, we investigate the diagnostic effectiveness of a classifier trained on a set of features derived from single-channel airflow

measurements. The features considered are based on recurrence quantification analysis (RQA) of the measurement time series and are

optionally augmented with single measurements of neck circumference and body mass index. The airflow measurement utilized is the nasal

pressure (NP). The study used an overnight recording from each of 77 patients undergoing PSG testing. Mixture discriminate analysis was

used to obtain a classifier, which predicts whether or not a measurement segment contains an SDB event. Patients were diagnosed as having

SDB disease if the recording contained measurement segments predicted to include an SDB event at a rate exceeding a threshold value. A

patient can be diagnosed as having SDB disease if the rate of SDB events per hour of sleep, the respiratory disturbance index (RDI), is = 15 or

sometimes = 5. Here we trained and evaluated the classifier under each assumption, obtaining areas under receiver operating curves using

fivefold cross-validation of 0.96 and 0.93, respectively. We used a two-layer structure to select the optimal operating point and assess the

resulting classifier to avoid unbiased estimates. The resulting estimates for diagnostic sensitivity/specificity were 71.5%/89.5% for disease

classification when RDI = 15 and 63.3%/100% for RDI = 5. These results were found assuming that the costs of misclassifying healthy and

diseased subjects are equal, but we provide a framework to vary these costs. The results suggest that a classifier based on RQA features

derived from NP measurements could be used in an automated SDB screening device.

09 Multimodal Registration Procedure for the Initial Spatial Alignment of a Retinal Video Sequence to a Retinal Composite Image

Accurate placement of lesions is crucial for the effectiveness and safety of a retinal laser photocoagulation treatment. Computer assistance

provides the capability for improvements to treatment accuracy and execution time. The idea is to use video frames acquired from a scanning

digital ophthalmoscope (SDO) to compensate for retinal motion during laser treatment. This paper presents a method for the multimodal

registration of the initial frame from an SDO retinal video sequence to a retinal composite image, which may contain a treatment plan. The

retinal registration procedure comprises the following steps: 1) detection of vessel centerline points and identification of the optic disc; 2)

prealignment of the video frame and the composite image based on optic disc parameters; and 3) iterative matching of the detected vessel

Centerline points in expanding matching regions. This registration algorithm was designed for the initialization of a real-time registration

procedure that registers the subsequent video frames to the composite image. The algorithm demonstrated its capability to register various

pairs of SDO video frames and composite images acquired from patients.





10 Model Fitting Using RANSAC for Surgical Tool Localization in 3-D Ultrasound Images

Ultrasound guidance is used for many surgical interventions such as biopsy and electrode insertion. We present a method to localize a thin

surgical tool such as a biopsy needle or a microelectrode in a 3-D ultrasound image. The proposed method starts with thresholding and model

fitting using random sample consensus for robust localization of the axis. Subsequent localoptimization refines its position. Two different tool

image models are presented: one is simple and fast and the second uses learned a priori information about the tool’s voxel intensities and the

background. Finally, the tip of the tool is localized by finding an intensity drop along the axis. The simulation study shows that our algorithm

can localize the tool at nearly real-time speed, even using a MATLAB implementation, with accuracy better than 1 mm. In an experimental

comparison with several alternative localization methods, our method appears to be the fastest and the most robust one. We also show the

results on real 3-D ultrasound data from a PVA cryogel phantom, turkey breast, and breast biopsy.

11 Integrated Laser Doppler Blood Flow meter Designed to Enable Wafer-Level Packaging

The authors propose a new sensor structure for an integrated laser Doppler blood flow meter that consists of two silicon cavities with a PD

and laser diode inside each cavity. A silicon lid formed with a converging microlens completes the package. This structure, which was

achieved using micromachining techniques, features reduced optical power loss in the sensor, resulting in its small size and significantly low

power consumption. Measurements using a model tissue blood flow system confirmed that the new sensor had high linearity and a wide

dynamic range for measuring tissue blood flow.

12 Functional Morphology Analysis of the Left Anterior Descending Coronary Artery in EBCT Images

In this paper, we present a physics-based deformable model framework for morphological and motion analysis of the left anterior descending

(LAD) coronary artery. The proposed model is designed to capture the complex motion that the LAD undergoes during the cardiac cycle. The

key idea is to define a local coordinate system for the heart and to parameterize both the shape andmotion of the LADin a single framework.

The shape of theLADismodeled as a parametric generalized cylinder, and the motion during the heart cycle is modeled as a composite of three

components, which are as follows: 1) longitudinal deformation, 2) radial displacement, and 3) angular displacement over the cardiac cycle. The

proposed framework for the LAD shape–motion estimation is generic, since it does not assume any particular tubular shape. Results obtained

for four human subjects using electron beam computed tomography data are in agreement with LAD shape–motion deformations reported in

the literature.

13 Estimating Time-Varying Nonlinear Autoregressive Model Parameters by Minimizing Hyper surface Distance

A nonleast-squares (non-LS) based method is presented formodeling time-varying (TV) nonlinear systems. The proposed method combines

basis function technique and minimization of hypersurface distance (MHD) to combat TV and nonlinear dynamics, respectively. The

performance of TVMHD is compared to the LS and total LS methods using simulation examples as well as human heart rate data recorded

during different body positions. With all data, TVMHD significantly outperforms the two other methods by a factor of one order of magnitude;

the LS-based methods require double the number of parameters than TVMHD requires to obtain similar residual error values. The significance

of TVMHD is that due to its accurate parameter estimates concomitant with a fewer number of parameters, we now have the possibility of

pinpointing parameters that may be of physiological importance, where such application will be especially useful in discriminating diseased

conditions. Furthermore, our algorithm allows discrimination of model terms, which are TV or time invariant, by examining those basis

function coefficients that are designed to capture TV dynamics. However, it should be noted that the main disadvantage of TVMHD is that it

requires significantly greater computational time than the LS-based methods.

14 Detection and Analysis of the Intestinal Ischemia Using Visible and Invisible Hyper spectral Imaging





Intestinal ischemia, or inadequate blood flow to the intestine, is caused by a variety of disorders and conditions. The quickness with which the

problem is brought to medical attention for diagnosis and treatment has great effects on the outcome of ischemic injury. Recently, hyper

spectral sensors have advanced and emerged as compact imaging tools that can be utilized in medical diagnostics. Hyper spectral imaging

provides a powerful tool for noninvasive tissue analyses. In this paper, the hyper spectral camera, with visible and invisible wavelengths, has

been evaluated for detection and analysis of intestinal ischemia during surgeries. This technique can help the surgeon to quickly find ischemic

tissues. Two cameras, a visible-to-near-infrared camera (400–1000 nm) and an infrared camera (900–1700 nm) were used to capture the hyper

spectral images. Vessels supplying an intestinal segment of a pig were clamped to simulate ischemic conditions. A key wavelength range that

provides the best differentiation between normal and ischemic intestine was determined from all wavelengths that potentially reduces the

amount of data collected in subsequent work.The data were classified using two filters that were designed to discriminate the ischemic

intestinal regions.

15 Correntropy-Based Spectral Characterization of Respiratory Patterns in Patients with Chronic Heart Failure

A correntropy-based technique is proposed for the characterization and classification of respiratory flow signals in chronic heart failure (CHF)

patients with periodic or nonperiodic breathing (PB or nPB, respectively) and healthy subjects. The correntropy is a recently introduced,

generalized correlation measure whose properties lend them to the definition of a correntropybased spectral density (CSD). Using this

technique, both respiratory and modulation frequencies can be reliably detected at their original positions in the spectrum without prior

demodulation of the flow signal. Single-parameter classification of respiratory patterns is investigated for three different parameters extracted

from the respiratory and modulation frequency bands of the CSD, and one parameter defined by the correntropy mean. The results show that

the ratio between the powers in the modulation and respiratory frequency bands provides the best result when classifying CHFpatientswith

either PBor nPB, yielding an accuracy of 88.9%. The correntropy mean offers excellent performance when classifying CHF patients versus

healthy subjects, yielding an accuracy of 95.2% and discriminating nPB patients from healthy subjects with an accuracy of 94.4%.

16 Automatic Area Classification in Peripheral Blood Smears

Cell enumeration and diagnosis using peripheral blood smears are routine tasks in many biological and pathological examinations. Not every

area in the smear is appropriate for such tasks due to severe cell clumping or sparsity.Manual working-area selection is slow, subjective,

inconsistent, and statistically biased. Automatic working-area classification can reproducibly identify appropriate working smear areas.

However, very little research has been reported in the literature. With the aim of providing a preprocessing step for further detailed cell

enumeration and diagnosis for high-throughput screening (HTS), we propose an integrated algorithm for area classification and quantify both

cell spreading and cell clumping in terms of individual clumps and the occurrence probabilities of the group of clumps over the image.

Comprehensive comparisons are presented to compare the effect of these quantifications and their combinations. Our experiments using

images of Giemsa-stained blood smears show that the method is efficient, accurate (above 88.9% hit rates for all areas in the validation set of

140 images), and robust (above 78.1% hit rates for a test set of 4878 images). This lays a good foundation for fast working-area selection in

HTS

17 Analysis of Tidal Breat hing Flow Volume Loops for Automated Lung-Function Diagnosis in Infants

Lung-function analysis in the age group below 5 years has not yet found its way into clinical routine. One possible candidate for routine lung

testing in this age group is the analysis of tidal breathing flow-volume (TBFV) loops, a technique that has not yet proven to be capable ofdetecting obstructive and other lung disorders at an early stage. We present a new set of mathematical features useful to analyze TBFV loops.

These new features attempt to describe more complex properties of the loops, thus imitating medical judgment of the curves (e.g., “round,”

“triangular,” etc.) in a “linguistic” manner. Furthermore, we introduce support vector machines (SVMs) as a method for automated

classification of diseases. In a retrospective clinical trial on 195 spontaneously breathing infants aged 3 to 24 months, the discriminant power

of individual features and the overall diagnostic performance of SVMs is investigated and compared with the results obtained with traditional

Bayes’ classifiers. We demonstrate that the proposed new features perform better in all examined disease groups and that depending on the

disease, the classification error can be reduced by up to 50%. We conclude that TBFV loops may have a much stronger discriminant power

than previously thought.

elysium bio-medical 2010

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