A Hierarchical Method Based on Active Shape Models and Directed Hough Transform for Segmentation of Noisy Biomedical Images; Application in Segmentation of Pelvic X-ray Images

Background Traumatic pelvic injuries are often associated with severe, life-threatening hemorrhage, and immediate medical treatment is therefore vital. However, patient prognosis depends heavily on the type, location and severity of the bone fracture, and the complexity of the pelvic structure presents diagnostic challenges. Automated fracture detection from initial patient X-ray images can assist physicians in rapid diagnosis and treatment, and a first and crucial step of such a method is to segment key bone structures within the pelvis; these structures can then be analyzed for specific fracture characteristics. Active Shape Model has been applied for this task in other bone structures but requires manual initialization by the user. This paper describes a algorithm for automatic initialization and segmentation of key pelvic structures - the iliac crests, pelvic ring, left and right pubis and femurs - using a hierarchical approach that combines directed Hough transform and Active Shape Models. Results Performance of the automated algorithm is compared with results obtained via manual initialization. An error measures is calculated based on the shapes detected with each method and the gold standard shapes. ANOVA results on these error measures show that the automated algorithm performs at least as well as the manual method. Visual inspection by two radiologists and one trauma surgeon also indicates generally accurate performance. Conclusion The hierarchical algorithm described in this paper automatically detects and segments key structures from pelvic X-rays. Unlike various other x-ray segmentation methods, it does not require manual initialization or input. Moreover, it handles the inconsistencies between x-ray images in a clinical environment and performs successfully in the presence of fracture. This method and the segmentation results provide a valuable base for future work in fracture detection

Brain mapping and detection of functional patterns in fMRI using wavelet transform; application in detection of dyslexia

Background Functional Magnetic Resonance Imaging (fMRI) has been proven to be useful for studying brain functions. However, due to the existence of noise and distortion, mapping between the fMRI signal and the actual neural activity is difficult. Because of the difficulty, differential pattern analysis of fMRI brain images for healthy and diseased cases is regarded as an important research topic. From fMRI scans, increased blood ows can be identified as activated brain regions. Also, based on the multi-sliced images of the volume data, fMRI provides the functional information for detecting and analyzing different parts of the brain. Methods In this paper, the capability of a hierarchical method that performed an optimization algorithm based on modified maximum model (MCM) in our previous study is evaluated. The optimization algorithm is designed by adopting modified maximum correlation model (MCM) to detect active regions that contain significant responses. Specifically, in the study, the optimization algorithm is examined based on two groups of datasets, dyslexia and healthy subjects to verify the ability of the algorithm that enhances the quality of signal activities in the interested regions of the brain. After verifying the algorithm, discrete wavelet transform (DWT) is applied to identify the difference between healthy and dyslexia subjects. Results We successfully showed that our optimization algorithm improves the fMRI signal activity for both healthy and dyslexia subjects. In addition, we found that DWT based features can identify the difference between healthy and dyslexia subjects. Conclusion The results of this study provide insights of associations of functional abnormalities in dyslexic subjects that may be helpful for neurobiological identification from healthy subject

Architecture of the Short External Rotator Muscles of the Hip.

BackgroundMuscle architecture, or the arrangement of sarcomeres and fibers within muscles, defines functional capacity. There are limited data that provide an understanding of hip short external rotator muscle architecture. The purpose of this study was thus to characterize the architecture of these small hip muscles.MethodsEight muscles from 10 independent human cadaver hips were used in this study (n = 80 muscles). Architectural measurements were made on pectineus, piriformis, gemelli, obturators, quadratus femoris, and gluteus minimus. Muscle mass, fiber length, sarcomere length, and pennation angle were used to calculate the normalized muscle fiber length, which defines excursion, and physiological cross-sectional area (PCSA), which defines force-producing capacity.ResultsGluteus minimus had the largest PCSA (8.29 cm2) followed by obturator externus (4.54 cm2), whereas superior gemellus had the smallest PCSA (0.68 cm2). Fiber lengths clustered into long (pectineus - 10.38 cm and gluteus minimus - 10.30 cm), moderate (obturator internus - 8.77 cm and externus - 8.04 cm), or short (inferior gemellus - 5.64 and superior gemellus - 4.85). There were no significant differences among muscles in pennation angle which were all nearly zero. When the gemelli and obturators were considered as a single functional unit, their collective PCSA (10.00 cm2) exceeded that of gluteus minimus as a substantial force-producing group.ConclusionsThe key findings are that these muscles have relatively small individual PCSAs, short fiber lengths, and low pennation angles. The large collective PCSA and short fiber lengths of the gemelli and obturators suggest that they primarily play a stabilizing role rather than a joint rotating role

Integration of Attributes from Non-Linear Characterization of Cardiovascular Time-Series for Prediction of Defibrillation Outcomes

Objective The timing of defibrillation is mostly at arbitrary intervals during cardio-pulmonary resuscitation (CPR), rather than during intervals when the out-of-hospital cardiac arrest (OOH-CA) patient is physiologically primed for successful countershock. Interruptions to CPR may negatively impact defibrillation success. Multiple defibrillations can be associated with decreased post-resuscitation myocardial function. We hypothesize that a more complete picture of the cardiovascular system can be gained through non-linear dynamics and integration of multiple physiologic measures from biomedical signals. Materials and Methods Retrospective analysis of 153 anonymized OOH-CA patients who received at least one defibrillation for ventricular fibrillation (VF) was undertaken. A machine learning model, termed Multiple Domain Integrative (MDI) model, was developed to predict defibrillation success. We explore the rationale for non-linear dynamics and statistically validate heuristics involved in feature extraction for model development. Performance of MDI is then compared to the amplitude spectrum area (AMSA) technique. Results 358 defibrillations were evaluated (218 unsuccessful and 140 successful). Non-linear properties (Lyapunov exponent \u3e 0) of the ECG signals indicate a chaotic nature and validate the use of novel non-linear dynamic methods for feature extraction. Classification using MDI yielded ROC-AUC of 83.2% and accuracy of 78.8%, for the model built with ECG data only. Utilizing 10-fold cross-validation, at 80% specificity level, MDI (74% sensitivity) outperformed AMSA (53.6% sensitivity). At 90% specificity level, MDI had 68.4% sensitivity while AMSA had 43.3% sensitivity. Integrating available end-tidal carbon dioxide features into MDI, for the available 48 defibrillations, boosted ROC-AUC to 93.8% and accuracy to 83.3% at 80% sensitivity. Conclusion At clinically relevant sensitivity thresholds, the MDI provides improved performance as compared to AMSA, yielding fewer unsuccessful defibrillations. Addition of partial end-tidal carbon dioxide (PetCO2) signal improves accuracy and sensitivity of the MDI prediction model

Hydrogen peroxide filled poly(methyl methacrylate) microcapsules: potential oxygen delivery materials

This paper describes the synthesis of H2O2–H2O filled poly(methyl methacrylate) (PMMA) microcapsules as potential candidates for controlled O2 delivery. The microcapsules are prepared by a water-in-oil solvent emulsion and evaporation method. The results of this study describe the effect of process parameters on the characteristics of the microcapsules and on their in vitro performance. The size of the microcapsules, as determined from scanning electron microscopy, ranges from ∼5 to 30 μm and the size distribution is narrow. The microcapsules exhibit an internal morphology with entrapped H2O2–H2O droplets randomly distributed in the PMMA continuous phase. In vitro release studies of 4.5 wt% H2O2-loaded microcapsules show that ∼70% of the H2O2 releases in 24 h. This corresponds to a total O2 production of ∼12 cc/gram of dry microcapsules. Shelf-life studies show that the microcapsules retain ∼84 wt% of the initially loaded H2O2 after nine months storage at 2–8 °C, which is an attractive feature for clinical applications

On the physical origins of the negative index of refraction

The physical origins of negative refractive index are derived from a dilute microscopic model, producing a result that is generalized to the dense condensed phase limit. In particular, scattering from a thin sheet of electric and magnetic dipoles driven above resonance is used to form a fundamental description for negative refraction. Of practical significance, loss and dispersion are implicit in the microscopic model. While naturally occurring negative index materials are unavailable, ferromagnetic and ferroelectric materials provide device design opportunities.Comment: 4 pages, 1 figur

Fracture Detection in Traumatic Pelvic CT Images

Fracture detection in pelvic bones is vital for patient diagnostic decisions and treatment planning in traumatic pelvic injuries. Manual detection of bone fracture from computed tomography (CT) images is very challenging due to low resolution of the images and the complex pelvic structures. Automated fracture detection from segmented bones can significantly help physicians analyze pelvic CT images and detect the severity of injuries in a very short period. This paper presents an automated hierarchical algorithm for bone fracture detection in pelvic CT scans using adaptive windowing, boundary tracing, and wavelet transform while incorporating anatomical information. Fracture detection is performed on the basis of the results of prior pelvic bone segmentation via our registered active shape model (RASM). The results are promising and show that the method is capable of detecting fractures accurately

Lethal marine snow : pathogen of bivalve mollusc concealed in marine aggregates

Author Posting. © The Authors, 2005. This is the author's version of the work. It is posted here by permission of American Society of Limnology and Oceanography for personal use, not for redistribution. The definitive version was published in Limnology and Oceanography 50 (2005): 1983-1988, doi:10.4319/lo.2005.50.6.1983.We evaluated marine aggregates as environmental reservoirs for a thraustochytrid pathogen, Quahog Parasite Unknown (QPX), of the northern quahog or hard clam, Mercenaria mercenaria. Positive results from in situ hybridization and denaturing gradient gel electrophoresis confirm the presence of QPX in marine aggregates collected from coastal embayments in Cape Cod, Massachusetts, where QPX outbreaks have occurred. In laboratory experiments, aggregates were observed and recorded by entering a quahog’s pallial cavity, thereby delivering embedded particles from the water column to its benthic bivalve host. The occurrence of pathogen-laden aggregates in coastal areas experiencing repeated disease outbreaks suggests a means for the spread and survival of pathogens between epidemics and provides a specific target for environmental monitoring of those pathogens.This work was funded by an NSF grant as part of the joint NSF-NIH Ecology of Infectious Disease program, by the Woods Hole Oceanographic Institution (WHOI) Sea Grant Program, under a grant from the National Oceanic and Atmospheric Administration, U.S. Department of Commerce, and a National Science Foundation Graduate Fellowship to M. Lyons

Hemorrhage Detection and Segmentation in Traumatic Pelvic Injuries

Automated hemorrhage detection and segmentation in traumatic pelvic injuries is vital for fast and accurate treatment decision making. Hemorrhage is the main cause of deaths in patients within first 24 hours after the injury. It is very time consuming for physicians to analyze all Computed Tomography (CT) images manually. As time is crucial in emergence medicine, analyzing medical images manually delays the decision-making process. Automated hemorrhage detection and segmentation can significantly help physicians to analyze these images and make fast and accurate decisions. Hemorrhage segmentation is a crucial step in the accurate diagnosis and treatment decision-making process. This paper presents a novel rule-based hemorrhage segmentation technique that utilizes pelvic anatomical information to segment hemorrhage accurately. An evaluation measure is used to quantify the accuracy of hemorrhage segmentation. The results show that the proposed method is able to segment hemorrhage very well, and the results are promising