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Normalizing Videos of Anterior Eye Segment Surgeries. Quellec, Gwenole. Of Illinois at Chicago. Kotecha, Mrignayani, Univ. Of Illinois at Chicago. Single-Trial Classification of Neural Responses Evoked in Rapid Serial Visual Presentation: Effects of Stimulus Onset Asynchrony and Stimulus Repetition. Cecotti, Hubert. May 2, 2014 - 3 min - Uploaded by Suraj BPallavi Joshi is an Indian film and television actress.. She acted in films like Badla & Aadmi.

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Keywords:, Abstract: Hand human gesture recognition has been an important research topic widely studied around the world, as this field offers the ability to identify, recognize, and analyze human gestures in order to control devices or to interact with computer interfaces. In particular, in medical training, this approach is an important tool that can be used to obtain an objective evaluation of a procedure performance. In this paper, some obstetrical gestures, acquired by a forceps, were studied with the hypothesis that, as the scribbling and drawing movements, they obey the one-sixth power law, an empirical relationship which connects path curvature, torsion, and euclidean velocity.

Our results show that obstetrical gestures have a constant affine velocity, which is different for each type of gesture and based on this idea this quantity is proposed as an appropriate classification feature in the hand human gesture recognition field. Add to My Program Philips Res. Europe Center for Human Movement Sciences, Univ.

Medical Center Gr Department of Knowledge Engineering, Maastricht Univ Center for Human Movement Sciences, Univ. Medical Center Gr Philips Res. Of Movement and Sport Gerontology, German Sport Univ. Keywords:, Abstract: Falls result in substantial disability, morbidity, and mortality among older people. Early detection of fall risks and timely intervention can prevent falls and injuries due to falls. Simple field tests, such as repeated chair rise, are used in clinical assessment of fall risks in older people. Development of on-body sensors introduces potential beneficial alternatives for traditional clinical methods.

In this article, we present a pendant sensor based chair rise detection and analysis algorithm for fall risk assessment in older people. The recall and the precision of the transfer detection were 85% and 87% in standard protocol, and 61% and 89% in daily life activities.

Estimation errors of chair rise performance indicators: duration, maximum acceleration, peak power and maximum jerk were tested in over 800 transfers. Median estimation error in transfer peak power ranged from 1.9% to 4.6% in various tests.

Among all the performance indicators, maximum acceleration had the lowest median estimation error of 0% and duration had the highest median estimation error of 24% over all tests. The developed algorithm might be feasible for continuous fall risk assessment in older people. Sony Vegas Movie Studio Platinum 12 Serial Number.

Add to My Program Univ. Teknologi MARA Univ.

Teknologi MARA Univ. Teknologi MARA. Keywords:,, Abstract: Non-Structural Protein 1 (NS1) antigen has been recognized as a biomarker for diagnosis of flavivirus viral infections at early stage.

Surface Enhanced Raman Spectroscopy (SERS) is an optical technique capable of detecting up to a single molecule. Our previous work has established the Raman fingerprint of NS1 with gold as substrate. Our current study aims to classify NS1 infected saliva samples from healthy samples, a first ever attempt.

Saliva samples from healthy subjects, NS1 protein and NS1-saliva mixture samples were analyzed using SERS. The SERS spectra were then pre-processed prior to classification with support vector machine (SVM). NS1-saliva mixture at concentration of 10ppm, 50ppm and 100ppm were examined. Performance of SVM classifier with linear, polynomial and RBF kernels were compared, in term of accuracy, sensitivity, and specificity.

From the results, it can be concluded that SVM classifier is able to classify the samples into NS1 infected samples and normal saliva samples. Of the three kernels, performance in using polynomial and RBF kernel is found surpassing the linear kernel. The best performance is attained with RBF kernel with accuracy of [97.1% 93.4% 81.5%] for 100ppm, 50ppm and 10ppm respectively Add to My Program Univ. Of Wisconsin-Madison Univ. Of Wisconsin Univ. Of Wisconsin. Keywords:,, Abstract: Palpation plays a critical role in medical physical exams.

Despite the wide range of exams, there are several reproducible and subconscious sets of maneuvers that are common to examination by palpation. Previous studies by our group demonstrated the use of manikins and pressure sensors for measuring and quantifying how physicians palpate during different physical exams. In this study we develop mathematical models that describe some of these common maneuvers.

Dynamic pressure data was measured using a simplified testbed and different autoregressive models were used to describe the motion of interest. The frequency, direction and type of motion used were identified from the models. We believe these models can a provide better understanding of how humans explore objects in general and more specifically give insights to understand medical physical exams.

Add to My Program Bowie State Univ Bowie State Univ. Keywords: Abstract: As microarray data available to scientists continues to increase in size and complexity, it has become overwhelmingly important to find multiple ways to bring inference though analysis of DNA/mRNA sequence data that is useful to scientists. Though there have been many attempts to elucidate the issue of bringing forth biological inference by means of wavelet preprocessing and classification, there has not been a research effort that focuses on a cloud-based classification analysis of microarray data using Wavelet thresholding to identify significantly expressed features. This paper proposes a novel methodology that uses Wavelet based Denoising to initialize a threshold for determination of significantly expressed genes for classification. Additionally, this research was implemented and encompassed within cloud-based distributed processing environment.

The utilization of Cloud computing and Wavelet thresholding was used for the classification of 14 tumor classes from the Global Cancer Map (GCM). The results proved to be more accurate than using a predefined p-value for differential expression classification. This novel methodology analyzed Wavelet based threshold features of gene expression in a Cloud environment, furthermore classifying the expression of samples by analyzing gene patterns which inform us of biological processes. Furthermore, enabling researchers to face the present and forthcoming challenges that may arise in the analysis of data in functional genomics of large microarray datasets. Add to My Program National Univ.

Of Singapore Inst. For Infocomm Res Inst. For Infocomm Res Inst. For Infocomm Res National Univ. Of Singapore. Keywords:, Abstract: To address the nonstationarity issue in EEG-based brain computer interface (BCI), the computational model trained using the training data needs to adapt to the data from the test sessions. In this paper, we propose a novel adaptation approach based on the divergence framework.

Cross-session changes can be taken into consideration by searching the discriminative subspaces for test data on the manifold of orthogonal matrices in a semi-supervised manner. Subsequently, the feature space becomes more consistent across sessions and classifiers performance can be enhanced. Experimental results show that the proposed adaptation method yields improvements in classification performance. Add to My Program Chair: Univ. Of Pavia Co-Chair: Univ.

Degli Studi Di Milano Add to My Program Aichi Prefectural Univ Suzuka Univ. Of Medical Science Aichi Prefectural Univ Aichi Prefectural Univ. Keywords:,, Abstract: This paper proposed a method to monitor systolic blood pressure (BP) variability without using a cuff during the daytime. In this method, BP variability of long-term and short-term were separated and estimated respectively from features of phoplethysmograph (PPG) through the use of a frequency filter.

Then, total variability was obtained from the combination of long-term and short-term. BP by using a cuff (ground truth) and PPG of nine healthy young subjects were measured during the daytime; then BP variability was estimated from PPG to verify the validity of our method. As a result, the correlation coefficients between measured BP variability and estimated BP variability was improved from r = 0.35 in previous method to r = 0.41 in proposed method.

In particular, the estimation results in short-term BP variability showed good accuracy (r = 0.67). This method of estimating BP variability has the potential to be a simple and continuous BP monitoring system during the daytime.

Add to My Program Rochester Inst. Of Tech Shiraz Univ Rochester Inst. Keywords:,, Abstract: Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a sleep related breathing disorder that has important consequences in the health and development of infants and young children. To enhance the early detection of OSAHS, we propose a methodology based on automated analysis of nocturnal blood oxygen saturation (SpO2) from respiratory polygraphy (RP) at home. A database composed of 50 SpO2 recordings was analyzed. Three signal processing stages were carried out: (i) feature extraction, where statistical features and nonlinear measures were computed and combined with conventional oximetric indexes, (ii) feature selection using genetic algorithms (GAs), and (iii) feature classification through logistic regression (LR). Leave-one-out cross-validation (loo-cv) was applied to assess diagnostic performance.

The proposed method reached 80.8% sensitivity, 79.2% specificity, 80.0% accuracy and 0.93 area under the ROC curve (AROC), which improved the performance of single conventional indexes. Our results suggest that automated analysis of SpO2 recordings from at-home RP provides essential and complementary information to assist in OSAHS diagnosis in children. Add to My Program Eindhoven Univ. Of Tech Eindhoven Univ. Of Tech Philips Res Eindhoven Univ. Keywords:,, Abstract: Many healthcare and lifestyle applications could benefit from capacitive measurement systems for unobtrusive ECG monitoring.

However, a key technical challenge remains: the susceptibility of such systems to motion artifacts and common-mode interferences. With this in mind, we developed a novel method to reduce various types of artifacts present in capacitive ECG measurement systems. The objective is to perform ECG reconstruction and channel balancing in an automated and continuous manner. The proposed method consists of a) modeling the measurement system; b) specifically parameterizing the reconstruction equation; and c) adaptively estimating the parameters. A multi-frequency injection signal serves to estimate and track the variations of the different parameters of the reconstruction equation. A preliminary investigation on the validity of the method has been performed in both simulation and lab environment: the method shows benefits in terms of common-mode interference and motion artifact reduction, resulting in improved R-peak detection. Add to My Program Univ.

Of Pavia Univ. Of Pavia Massachusetts Inst.

Keywords:,, Abstract: Fetal Heart Rate (FHR) monitoring represents a powerful tool for checking the arousal of pathological fetal conditions during pregnancy. This paper proposes a multivariate approach for the discrimination of Normal and Intra Uterine Growth Restricted (IUGR) fetuses based on a small set of parameters computed on the FHR signal. We collected FHR recordings in a population of 120 fetuses (60 normals and 60 IUGRs) at approximately the same gestational week through a standard CTG non-stress test. A set of 8 linear and non-linear indices were selected and computed on each recording, on the basis of their “stand-alone” discriminative properties, demonstrated in previous studies. By using the Orange® data mining suite we checked various multivariate discrimination models. The results show that a Logistic Regression performed on a limited set of only 4 parameters can reach 92.5% accuracy in the correct identification of fetuses, with 93% sensitivity and 91.5% specificity.

Add to My Program TU Berlin Tech. Keywords:,, Abstract: Wearable monitoring systems have gained tremendous popularity in the health-care industry, opening new possibilities in diagnostic routines and medical treatments. Numerous hardware systems have been presented since, which allow for continuous acquisition of various biosignals like the ECG, PPG, EMG or EEG and which are suited for ambulatory settings.

Unfortunately, these flexible systems are liable to motion artifacts and especially photoplethysmographic signals are seriously distorted when the patient is not at rest. A lot of work has been done to reduce artifacts and noise, ranging from simple filtering methods to very complex statistical approaches. With regard to the PPG, certain quality indices have been proposed to evaluate the signal conditions. As movements are the primary source of signal disturbances, the relation between the output of a signal quality estimator and acceleration data captured directly on the PPG sensor is focused in this work. It will be shown that typical motions can be detected on-line, thereby providing additional information which will significantly improve signal quality assessments. Add to My Program Chair: Univ. Of Warwick Co-Chair: The Mind Res.

Of New Mexico Add to My Program Philips Res Philips Res. North America Univ. Of Twente Univ.

Of Wisconsin. Keywords:,, Abstract: Automatic sleep staging on an online basis has recently emerged as a research topic motivated by fundamental sleep research. The aim of this paper is to find optimal signal processing methods and machine learning algorithms to achieve online sleep staging on the basis of a single EEG signal. The classification performance obtained using six different EEG signals and various signal processing feature sets is compared using the kappa statistic which has very recently became popular in sleep staging research.

A variable duration of the EEG segment (or epoch) to decide on the sleep stage is also analyzed. Spectral-domain, time-domain, linear, and nonlinear features are compared in terms of performance and two types of machine learning approaches (random forests and support vector machines) are assessed.

We have determined that frontal EEG signals, with spectral linear features, an epoch duration in between 18 and 30 seconds, and a random forest classifier lead to optimal classification performance while ensuring real-time online operation. Add to My Program IIT Hyderabad Maulana Azad National Inst. Of Tech Maulana Azad National Inst. Of Tech IIT Hyderabad Indian Inst. Hyderabad Univ.

Keywords:,, Abstract: Autism Spectrum Disorder (ASD) is a neural development disorder affecting the information processing capability of the brain by altering how nerve cells and their synapses interconnect and organize. Electroencephalograph or EEG signals records the electrical activity of the brain from the scalp which can be utilized to identify and investigate the brain wave pattern which are specific to individuals with ASD. Therefore, the analysis of ASD can be done by scrutinizing the specific bands (Theta, Mu and Beta) of the EEG signal.

However, EEG signals are mainly contaminated by Ocular (Eye-blink) and Myogenic artefacts which pose problems in EEG interpretation. In this paper an automated real-time method for detection and removal of Ocular and Myogenic artefacts for multichannel EEG signal is proposed which would enhance the diagnostic accuracy. The proposed methodology has been validated against 20 subjects from Caltech, Physionet, Swartz Center for Computational Neuroscience and the computed average correlation and regression are 0.7574 and 0.6992 respectively. Add to My Program The Univ. Of Melbourne Khalifa Univ. Of Science, Tech.

And Res Tohoku Univ Tohoku Univ The Univ. Of Melbourne. Keywords:,, Abstract: Fetal cardiac assessment techniques are aimed to identify fetuses at risk of intrauterine compromise or death. Evaluation of the electromechanical coupling as a fundamental part of the fetal heart physiology, provides valuable information about the fetal wellbeing during pregnancy.

It is based on the opening and closing time of the cardiac valves and the onset of the QRS complex of the fetal electrocardiogram (fECG). The focus of this paper is on the automated identification of the fetal cardiac valve opening and closing from Doppler Ultrasound signal and fECG as a reference. To this aim a novel combination of Emprical Mode Decomposition (EMD)and multi-dimensional Hidden Markov Models (MD-HMM)was employed which provided beat-to-beat estimation of cardiac valve event timings with improved precision (82.9%) compared to the one dimensional HMM (77.4%) and hybrid HMM-Suppeort Vector Machine (SVM) (79.8%) approaches. Add to My Program GIPSA-Lab GIPSA-Lab Grenoble Univ UJF-Grenoble 1 / CNRS / TIMC-IMAG UMR 5525 Univ. Joseph Fourier Univ. Keywords:, Abstract: Quasi-periodic signals can be modeled by their second order statistics as Gaussian process.

This work presents a non-parametric method to model such signals. ECG, as a quasi-periodic signal, can also be modeled by such method which can help to extract the fetal ECG from the maternal ECG signal, using a single source abdominal channel. The prior information on the signal shape, and on the maternal and fetal RR interval, helps to better estimate the parameters while applying the Bayesian principles. The values of the parameters of the method, among which the R-peak instants, are accurately estimated using the Metropolis-Hastings algorithm. This estimation provides very precise values for the R-peaks, so that they can be located even between the existing time samples. Add to My Program Univ.

Of Auckland Univ. Of Auckland Univ. Of Auckland The Univ. Keywords:,, Abstract: There is approximately a 6-8 hour window that exists from when a hypoxic-ischemic insult occurs, in utero, before significant irreversible brain injury occurs in new born infants. The focus of our work is to determine through the electroencephalogram (EEG) if such a hypoxic-ischemic insult has occurred such that neuro-protective treatment can be sought within this period.

At present, there are no defined biomarkers in the EEG that are currently being used to help classify if a hypoxic ischemia insult has occurred. However, micro-scale transients in the form of spikes, sharps and slow waves exists that could provide precursory information whether a hypoxic-ischemic insult has occurred or not. In our previous studies we have successfully automatically identified spikes with high sensitivity and selectivity in the conventional 64Hz sampled EEG. This paper details the significant advantage that can be obtained in using high frequency 1024Hz sampled EEG for sharp wave detection over the typically employed 64Hz sampled EEG. This advantage is amplified when a combination of wavelet Type-2 Fuzzy Logic System (WT-Type-2-FLS) classifiers are used to identify the sharp wave transients. By applying WT-Type-2-FLS to the 1024Hz EEG record and to the same down-sampled 64Hz EEG record we demonstrate, how the sharp wave transients detection increases significantly for high resolution 1024Hz EEG over 64Hz EEG. The WT-Type-2-FLS algorithm performance was assessed over 3 standardised time periods within the first 8 hours, post occlusion of a fetal sheep, in utero.

1024Hz EEG results demonstrate the algorithm detected sharps with overall performance rates of 85%, 92%, and 87% in the Early/Mid and Late-latent phases of injury, respectively as compared to 25%, 55% and 31% in the 64Hz EEG. These results demonstrate the power of Wavelet Type-2 Fuzzy Logic System at detecting sharp waves in 1024Hz EEG and suggest that there should be a movement toward micro-scale transients detection. Add to My Program Vital Connect Inc Vital Connect, Inc. Keywords:, Abstract: Polysomnography (PSG) is the gold standard that manually quantifies the apnea-hypopnea index (AHI) to assess the severity of sleep apnea syndrome (SAS). This study presents an algorithm that automatically estimates the AHI value using a disposable HealthPatchTM sensor.

Volunteers (n=53, AHI: 0.1-85.8) participated in an overnight PSG study with patch sensors attached to their chest at three specified locations and data were wirelessly acquired. Features were computed for 150-second epochs of patch sensor data using analyses of heart rate variability, respiratory signals, posture and movements. Linear Support Vector Machine classifier was trained to detect the presence/absence of apnea/hypopnea events for each epoch. The number of epochs identified with events was subsequently mapped to AHI values using quadratic regression analysis. The classifier and regression models were optimized to minimize the mean-square error of AHI based on leave-one-out cross-validation. Comparison of predicted and reference AHI values offered the linear correlation coefficients as (0.87, 0.88 and 0.92) for the three locations, respectively. The predicted AHI values were subsequently used to classify the control-to-mild apnea group (AHI.

Keywords: Abstract: Cerebral palsy is caused by an injury to the brain at or near the time of birth and impairs movement and coordination. Every brain injury is unique and quantifying patient-specific changes in neuromuscular control remains challenging. We used a combination of muscle synergy analysis and musculoskeletal simulation to evaluate altered neuromuscular control in cerebral palsy. The K12 Interdisciplinary Rehabilitation Engineering Career Development Award enabled this research by providing mentorship in neuropathophysiology and opportunities to connect with clinicians.

Add to My Program Univ. Of Wisconsin-Milwaukee Univ. Of Wisconsin-Milwaukee Univ. Of Wisconsin - Milwaukee Shriners Hospital for Children-Chicago Marquette Univ. Keywords: Abstract: This NIH K12* research examined experimental and computational methods to assess shoulder joint kinematics during manual wheelchair mobility in pediatric spinal cord injury (SCI).

Results demonstrated minimal differences between propulsion biomechanics in the dominant and non-dominant shoulder of six children. Quantitative methods such as these may assist clinicians and rehabilitation engineers in providing advanced transitional care for children with SCI through prescription, training, and long-term usage of manual wheelchairs. Protected time and mentorship providedthrough the K12 award made this work possible. Add to My Program Univ.

Keywords: Abstract: The ultimate goal of an upper limb prosthesis is to replace the function of the missing limb. However current prosthetic technology is not able to do this fully. In the Rehabilitation Biomechanics Laboratory, we focus on assessing performance with upper limb prostheses so we might guide the design and evaluation of future prosthetic devices. The K12 Interdisciplinary Rehabilitation Engineering Career Development Award has helped enable this research by providing important mentoring support in clinical research and protected time from teaching to focus on furthering these research efforts. Add to My Program Univ.

Of Texas at Austin Univ. Of Texas at Austin. Keywords: Abstract: Physical therapy following neuromuscular insult such as stroke intervenes at the limbs despite the source of the problem in the brain. Recently, functional magnetic resonance imaging in real time (rtfMRI) neurofeedback has been used as promising neural intervention to guide mental imagery towards self-regulation of selected brain circuitry.

This paradigm provides some unique challenges, including quantification of how the self-control is enabled. In this abstract, we outline the motivation for the concept of neurally guided physical therapy, where physical movements are modulated in relation to activity in the brain. Add to My Program Univ. Of Southern California. Keywords: Abstract: Research in movement and rehabilitation science (MRS) often relies on theories derived from engineering to both understand the control of human movement and improve function in clinical populations.

As a scholar with the Interdisciplinary Rehabilitation Engineering Career Development Program, I have had the opportunity to develop as an MRS researcher through mentorship by senior scholars in both engineering and clinical science. Here, I describe the current developments in my research program whose overall objective is to understand how locomotion is controlled and adapted in both the healthy and injured neuromuscular system.

Neurological disorders such as stroke and Parkinson’s disease often result in gait impairments that put these individuals at a higher risk for falling. Ultimately, reducing fall risk requires 1) an understanding of how the neuromuscular system controls dynamic stability, and 2) development of novel strategies that lead to sustained improvements in function. Here, I present two studies that seek to address each of these aims. Add to My Program UNC-Chapel Hill & NCSU. Keywords: Abstract: Injury-induced functional reorganization within the cortex may, in part, facilitate recovery. Progress in understanding the cellular basis of this reorganization has been limited due to the difficulty in accessing and manipulating long projection neurons within the complex central nervous system (CNS). This paper describes my progress as part of a K12 career development award to use in vitro engineering-based approaches to examine injury-induced neuronal and synaptic remodeling.

Add to My Program Chair: Emory Univ Co-Chair: The Univ. Of Chicago Add to My Program Univ. Of Sydney Univ. Of Sydney Royal Prince Alfred Hospital The Univ. Keywords:,, Abstract: This paper proposes a framework to assess the potential value of 99mTc Sestamibi SPECT in addition to Gadolinium-enhanced MRI for the monitoring of patients with high grade gliomas under antiangiogenic treatment. It includes: 1) multimodal and monomodal high precision registration steps achieved thanks to a registration strategy which selects the best method among several ones for each dataset, 2) tumor segmentation steps dedicated to each modality and 3) a tumor comparison step which consists in the computation of some global (volume, intensity) and local (matching and mismatching) quantitative indices to analyze the tumor using different imaging modalities and at different times during the treatment.

Each step is checked via 2D and 3D visualization. This framework was applied to a database of fifteen patients. For all patients, except one, the tumor volumes decrease globally and locally. Furthermore, a high correlation (r=0.77) was observed between MRI and Sestamibi tumor volumes. Finally, local indices show some possible mismatches between MRI Gadolinium uptake and Sestamibi uptake, which need to be further investigated. Add to My Program National Inst. His 945gc Motherboard Audio Drivers.

Of Health National Inst. Of Health National Inst. Of Health National Inst. Keywords:,, Abstract: Open source software advances research and allows for more application specific tuning in a very feasible and efficient manner. Chief among the open source platforms is the MATLAB programming environment which is very commonly used in research settings especially in the field of medical imaging. Although many scientists use MATLAB for analysis, there remains a significant lack of open source software platforms which allow for both the analysis and visualization of pathological regions within fused multi-modal medical images. There is a clear need for a MATLAB based software which allows for anatomical (i.e.

CT or MRI) and functional (i.e. PET) joint visualization and quantification in an intuitive and efficient way. In particular, this software should focus on PET imaging which is being more widely used for small animal studies. We present here the structure, features, and potential applications of an open source software for the Quantitative Analysis and Visualization of PET Images (QAV-PET).

Add to My Program Univ. Of Sydney Univ. Of Sydney The Univ.

Of Sydney Royal Prince Alfred Hospital. Keywords:,, Abstract: Fluorodeoxyglucose positron emission tomography – computed tomography (FDG PET-CT) is the preferred image modality for lymphoma diagnosis. Sites of disease generally appear as foci of increased FDG uptake. Thresholding methods are often applied to robustly separate these regions.

However, its main limitation is that it also includes sites of FDG excretion and physiological FDG uptake regions, which we define as FEPU – sites of FEPU include the bladder, renal, papillae, ureters, brain, heart and brown fat. FEPU can make image interpretation problematic. The ability to identify and label FEPU sites and separate them from abnormal regions is an important process that could improve image interpretation. We propose a new method to automatically separate and label FEPU sites from the thresholded PET images. Our method is based on the selective use of features extracted from data types comprising of PET, CT and PET-CT. Our FEPU classification of 43 clinical lymphoma patient studies revealed higher accuracy when compared to non-selective image features. Add to My Program Zhejiang Univ Zhejiang Univ HKUST Zhejiang Univ.

Keywords:, Abstract: Low dose positron emission tomography(PET) reconstruction remains a challenging issue for statistical PET reconstruction methods due to the low SNR of data. Due to the ill-conditioning of image reconstruction, proper prior knowledge should be incorporated to constrain the reconstruction.

Since PET images are piecewise smoothing, we propose the total variational (TV) minimization based algorithm for low dose PET imaging. The fundamental power of this strategy rests with the edge locations of important image features tend to be preserved thanks to TV regularization. In addition, a new computational method have been employed with improved computational speed and robustness. Experimental results on Monte Carlo simulations demonstrate its superior performance.

Add to My Program Tech. Educational Inst. Of Athens Univ.

Of Patras Univ. Keywords:, Abstract: Monte Carlo (MC) simulations play a crucial role in nuclear medical imaging since they can provide the ground truth for clinical acquisitions, by integrating and quantifing all physical parameters that affect image quality. The last decade a number of realistic computational anthropomorphic models have been developed to serve imaging, as well as other biomedical engineering applications.

The combination of MC techniques with realistic computational phantoms can provide a powerful tool for pre and post processing in imaging, data analysis and dosimetry. This work aims to create a global database for simulated Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) exams and the methodology, as well as the first elements are presented.

Simulations are performed using the well validated GATE opensource toolkit, standard anthropomorphic phantoms and activity distribution of various radiopharmaceuticals, derived from literature. The resulting images, projections and sinograms of each study are provided in the database and can be further exploited to evaluate processing and reconstruction algorithms. Patient studies using different characteristics are included in the database and different computational phantoms were tested for the same acquisitions.

These include the XCAT, Zubal and the Virtual Family, which some of which are used for the first time in nuclear imaging. The created database will be freely available and our current work is towards its extension by simulating additional clinical pathologies. Add to My Program Chair: Univ. Of Missouri Co-Chair: Univ. Of Washington Add to My Program Virginia Commonwealth Univ Virginia Commonwealth Univ Virginia Commonwealth Univ Temple Univ Univ. Of Michigan Univ.

Of Michigan - Ann Arbor Virginia Commonwealth Univ. Keywords:,, Abstract: Dental caries are one of the most prevalent chronic diseases. The management of dental caries demands detection of carious lesions at early stages.

This study aims to design an automated system to detect and score caries lesions based on optical images of the occlusal tooth surface according to the International Caries Detection and Assessment System (ICDAS) guidelines. The system detects the tooth boundaries and irregular regions, and extracts 77 features from each image. These features include statistical measures of color space, grayscale image, as well as Wavelet Transform and Fourier Transform based features. 88 photographs of occlusal surface of extracted teeth, examined and scored by ICDAS experts, were used in this study. Seven ICDAS codes which show the different stages in caries development were collapsed into three classes: score 0, scores 1 and 2, and scores 3 to 6. The system shows accuracy of 86.3%, specificity of 91.7%, and sensitivity of 83.0% in ten-fold cross validation in classification of the tooth images. While the system needs further improvement and validation using larger datasets, it presents promising potential for clinical diagnostics with high accuracy and minimal cost.

This is a notable advantage over existing systems requiring expensive imaging and external hardware. Add to My Program National and Tech. Of Athens Biomedical Engineering Lab. National Tech. Of Paediatric Dentistry, Dental School, National and Kapod Univ. Of Ioannina National Tech. Of Athens Biomedical Engineering Lab.

School of Electrical and Comp Dept. Of Paediatric Dentistry, Dental School, National and Kapod Univ. Keywords: Abstract: The aim of this work is to present a modification of the Random Walker algorithm for the segmentation of occlusal caries from photographic color images.The modification improves the detection and time execution performance of the classical Random Walker algorithm and also deals with the limitations and difficulties that the specific type of images impose to the algorithm. The proposed modification consists of eight steps: 1) definition of the seed points, 2) conversion of the image to gray scale, 3) application of watershed transformation, 4) computation of the centroid of each region, 5) construction of the graph, 6) application of the Random Walker algorithm, 7) smoothing and extraction of the perimeter of the regions of interest and 8) overlay of the results. The algorithm was evaluated using a set of 96 images where 339 areas of interest were manually segmented by an expert. The obtained segmentation accuracy is 93%.

Add to My Program Georgia Inst. Of Tech Georgia Inst. Of Tech Georgia Inst. Of Tech Emory Univ. School of Medicine Georgia Tech. And Emory Univ.

Keywords: Abstract: Clinical decision support systems use image processing and machine learning methods to objectively predict cancer in histopathological images. Integral to the development of machine learning classifiers is the ability to generalize from training data to unseen future data. A classification model’s ability to accurately predict class label for new unseen data is measured by performance metrics, which also informs the classifier model selection process. Based on our research, commonly used metrics in literature (such as accuracy, ROC curve) do not accurately reflect the trained model’s robustness. To the best of our knowledge, no research has been conducted to quantitatively compare performance metrics in the context of cancer prediction in histopathological images. In this paper, we evaluate various performance metrics and show that the Lift metric has the highest correlation between internal and external validation sets of a nested cross validation pipeline (R^2 = 0.57). Thus, we demonstrate that the Lift metric best generalizes classifier performance among the 23 metrics that were evaluated.

Using the lift metric, we develop a classifier with a misclassification rate of 0.25 (4-class classifier) for data that the model was not trained on (external validation). Add to My Program Univ. Of Auvergne Clermont Univ. D' Auvergne Univ. D'auvergne - Inst. Of Tech HISTALIM HISTALIM CHU Estaing, Clermont-Ferrand CHU Estaing, Clermont-Ferrand CHU Estaing, Clermont-Ferrand. Keywords:,, Abstract: The majority of existing computer-aided diagnosis (CAD) schemes for Alzheimer's disease (AD) relies on the analysis of biomarkers at a single time-point, ignoring the progressive nature of the disorder.

Recently, a method was proposed by Gray et al. [1] for the multi-region analysis of longitudinal fuorodeoxyglucose positron emission tomography (FDG-PET) images which reported classification improvements in using regional signal intensities combined with regional change over a 12 month period.

In this paper we extend the approach proposed in [1] to the analysis of the entire brain pattern. Compared to [1], our method uses voxel-wise differences and avoids segmentation of the images into regions of interest. For our study, FDG-PET scans at the baseline and at 12-month follow-up of 66 cognitively normal (CN), 109 MCI and 48 AD subject were extracted from the Alzheimer's disease neuroimaging initiative (ADNI) database.

For both AD and MCI identification, the best classification results were achieved by combining cross-sectional and longitudinal information rather than using only the cross-sectional information. Furthermore, the longitudinal voxel-based analysis outperformed multi-region analysis. Add to My Program Coll. Of Engineering Guindy, Anna Univ ANNA Univ.