Jesus J. Caban

Texture-based Transfer Functions for Direct Volume Rendering

Visualization of volumetric data faces the difficult task of finding effective parameters for the transfer functions. Those parameters can determine the effectiveness and accuracy of the visualization. Frequently, volumetric data includes multiple structures and features that need to be differentiated. However, if those features have the same intensity and gradient values, existing transfer functions are limited at effectively illustrating those similar features with different rendering properties. We introduce texture-based transfer functions for direct volume rendering. In our approach, the voxelpsilas resulting opacity and color are based on local textural properties rather than individual intensity values. For example, if the intensity values of the vessels are similar to those on the boundary of the lungs, our texture-based transfer function will analyze the textural properties in those regions and color them differently even though they have the same intensity values in the volume. The use of texture-based transfer functions has several benefits. First, structures and features with the same intensity and gradient values can be automatically visualized with different rendering properties. Second, segmentation or prior knowledge of the specific features within the volume is not required for classifying these features differently. Third, textural metrics can be combined and/or maximized to capture and better differentiate similar structures. We demonstrate our texture-based transfer function for direct volume rendering with synthetic and real-world medical data to show the strength of our technique.

Radio Frequency Identification Systems Technology in the Surgical Setting

Radio frequency identification (RFID) is a technology that will have a profound impact on medicine and the operating room of the future. The purpose of this article is to provide an introduction to this exciting technology and a description of the problems in the perioperative environment that RFID might address to improve safety and increase productivity. Although RFID is still a nascent technology, applications are likely to become much more visible in patient care and treatment areas and will raise questions for practitioners. We also address both the current limitations and what appear to be reasonable near-future possibilities.

Computer-assisted detection of infectious lung diseases: a review.

Respiratory tract infections are a leading cause of death and disability worldwide. Although radiology serves as a primary diagnostic method for assessing respiratory tract infections, visual analysis of chest radiographs and computed tomography (CT) scans is restricted by low specificity for causal infectious organisms and a limited capacity to assess severity and predict patient outcomes. These limitations suggest that computer-assisted detection (CAD) could make a valuable contribution to the management of respiratory tract infections by assisting in the early recognition of pulmonary parenchymal lesions, providing quantitative measures of disease severity and assessing the response to therapy. In this paper, we review the most common radiographic and CT features of respiratory tract infections, discuss the challenges of defining and measuring these disorders with CAD, and propose some strategies to address these challenges.

Texture-based feature tracking for effective time-varying data visualization

Analyzing, visualizing, and illustrating changes within time-varying volumetric data is challenging due to the dynamic changes occurring between timesteps. The changes and variations in computational fluid dynamic volumes and atmospheric 3D datasets do not follow any particular transformation. Features within the data move at different speeds and directions making the tracking and visualization of these features a difficult task. We introduce a texture-based feature tracking technique to overcome some of the current limitations found in the illustration and visualization of dynamic changes within time-varying volumetric data. Our texture-based technique tracks various features individually and then uses the tracked objects to better visualize structural changes. We show the effectiveness of our texture-based tracking technique with both synthetic and real world time-varying data. Furthermore, we highlight the specific visualization, annotation, registration, and feature isolation benefits of our technique. For instance, we show how our texture-based tracking can lead to insightful visualizations of time-varying data. Such visualizations, more than traditional visualization techniques, can assist domain scientists to explore and understand dynamic changes.

Rapid Development of Medical Imaging Tools with Open-Source Libraries

Rapid prototyping is an important element in researching new imaging analysis techniques and developing custom medical applications. In the last ten years, the open source community and the number of open source libraries and freely available frameworks for biomedical research have grown significantly. What they offer are now considered standards in medical image analysis, computer-aided diagnosis, and medical visualization. A cursory review of the peer-reviewed literature in imaging informatics (indeed, in almost any information technology-dependent scientific discipline) indicates the current reliance on open source libraries to accelerate development and validation of processes and techniques. In this survey paper, we review and compare a few of the most successful open source libraries and frameworks for medical application development. Our dual intentions are to provide evidence that these approaches already constitute a vital and essential part of medical image analysis, diagnosis, and visualization and to motivate the reader to use open source libraries and software for rapid prototyping of medical applications and tools.

Visual analytics in healthcare--opportunities and research challenges.

As medical organizations modernize their operations, they are increasingly adopting electronic health records (EHRs) and deploying new health information technology systems that create, gather, and manage their information. As a result, the amount of data available to clinicians, administrators, and researchers in the healthcare system continues to grow at an unprecedented rate.1 However, despite the substantial evidence showing the benefits of EHR adoption, e-prescriptions, and other components of health information exchanges, healthcare providers often report only modest improvements in their ability to make better decisions by using more comprehensive clinical information.2,3 The large volume of clinical data now being captured for each patient poses many challenges to (a) clinicians trying to combine data from different disparate systems and make sense of the patient’s condition within the context of the patient’s medical history, (b) administrators trying to make decisions grounded in data, (c) researchers trying to understand differences in population outcomes, and (d) patients trying to make use of their own medical data. In fact, despite the many hopes that access to more information would lead to more informed decisions, access to comprehensive and large-scale clinical data resources has instead made some analytical processes even more difficult.4 Visual analytics is an emerging discipline that has shown significant promise in addressing many of these information overload challenges. Visual analytics is the science of analytical reasoning facilitated by advanced interactive visual interfaces.5 In order to facilitate …

A graph-theoretic approach for segmentation of PET images

Segmentation of positron emission tomography (PET) images is an important objective because accurate measurement of signal from radio-tracer activity in a region of interest is critical for disease treatment and diagnosis. In this study, we present the use of a graph based method for providing robust, accurate, and reliable segmentation of functional volumes on PET images from standardized uptake values (SUVs). We validated the success of the segmentation method on different PET phantoms including ground truth CT simulation, and compared it to two well-known threshold based segmentation methods. Furthermore, we assessed intra-and inter-observer variation in delineation accuracy as well as reproducibility of delineations using real clinical data. Experimental results indicate that the presented segmentation method is superior to the commonly used threshold based methods in terms of accuracy, robustness, repeatability, and computational efficiency.

Findings from Structural MR Imaging in Military Traumatic Brain Injury

PURPOSE To describe the initial neuroradiology findings in a cohort of military service members with primarily chronic mild traumatic brain injury (TBI) from blast by using an integrated magnetic resonance (MR) imaging protocol. MATERIALS AND METHODS This study was approved by the Walter Reed National Military Medical Center institutional review board and is compliant with HIPAA guidelines. All participants were military service members or dependents recruited between August 2009 and August 2014. There were 834 participants with a history of TBI and 42 participants in a control group without TBI (not explicitly age- and sex-matched). MR examinations were performed at 3 T primarily with three-dimensional volume imaging at smaller than 1 mm(3) voxels for the structural portion of the examination. The structural portion of this examination, including T1-weighted, T2-weighted, before and after contrast agent administrtion T2 fluid attenuation inversion recovery, and susceptibility-weighted images, was evaluated by neuroradiologists by using a modified version of the neuroradiology TBI common data elements (CDEs). Incident odds ratios (ORs) between the TBI participants and a comparison group without TBI were calculated. RESULTS The 834 participants were diagnosed with predominantly chronic (mean, 1381 days; median, 888 days after injury) and mild (92% [768 of 834]) TBI. Of these participants, 84.2% (688 of 817) reported one or more blast-related incident and 63.0% (515 of 817) reported loss of consciousness at the time of injury. The presence of white matter T2-weighted hyperintense areas was the most common pathologic finding, observed in 51.8% (432 of 834; OR, 1.75) of TBI participants. Cerebral microhemorrhages were observed in a small percentage of participants (7.2% [60 of 834]; OR, 6.64) and showed increased incidence with TBI severity (P < .001, moderate and severe vs mild). T2-weighted hyperintense areas and microhemorrhages did not collocate by visual inspection. Pituitary abnormalities were identified in a large proportion (29.0% [242 of 834]; OR, 16.8) of TBI participants. CONCLUSION Blast-related injury and loss of consciousness is common in military TBI. Structural MR imaging demonstrates a high incidence of white matter T2-weighted hyperintense areas and pituitary abnormalities, with a low incidence of microhemorrhage in the chronic phase.

Automatic Detection and Quantification of Tree-in-Bud (TIB) Opacities From CT Scans

This study presents a novel computer-assisted detection (CAD) system for automatically detecting and precisely quantifying abnormal nodular branching opacities in chest computed tomography (CT), termed tree-in-bud (TIB) opacities by radiology literature. The developed CAD system in this study is based on 1) fast localization of candidate imaging patterns using local scale information of the images, and 2) Möbius invariant feature extraction method based on learned local shape and texture properties of TIB patterns. For fast localization of candidate imaging patterns, we use ball-scale filtering and, based on the observation of the pattern of interest, a suitable scale selection is used to retain only small size patterns. Once candidate abnormality patterns are identified, we extract proposed shape features from regions where at least one candidate pattern occupies. The comparative evaluation of the proposed method with commonly used CAD methods is presented with a dataset of 60 chest CTs (laboratory confirmed 39 viral bronchiolitis human parainfluenza CTs and 21 normal chest CTs). The quantitative results are presented as the area under the receiver operator characteristics curves and a computer score (volume affected by TIB) provided as an output of the CAD system. In addition, a visual grading scheme is applied to the patient data by three well-trained radiologists. Interobserver and observer-computer agreements are obtained by the relevant statistical methods over different lung zones. Experimental results demonstrate that the proposed CAD system can achieve high detection rates with an overall accuracy of 90.96%. Moreover, correlations of observer-observer (R2=0.8848, p <; 0.01) and observer-CAD agreements (R2=0.824, p <; 0.01) validate the feasibility of the use of the proposed CAD system in detecting and quantifying TIB patterns.

Case Study on Visualizing Hurricanes Using Illustration-Inspired Techniques

The devastating power of hurricanes was evident during the 2005 hurricane season, the most active season on record. This has prompted increased efforts by researchers to understand the physical processes that underlie the genesis, intensification, and tracks of hurricanes. This research aims at facilitating an improved understanding into the structure of hurricanes with the aid of visualization techniques. Our approach was developed by a mixed team of visualization and domain experts. To better understand these systems, and to explore their representation in NWP models, we use a variety of illustration-inspired techniques to visualize their structure and time evolution. Illustration-inspired techniques aid in the identification of the amount of vertical wind shear in a hurricane, which can help meteorologists predict dissipation. Illustration-style visualization, in combination with standard visualization techniques, helped explore the vortex rollup phenomena and the mesovortices contained within. We evaluated the effectiveness of our visualization with the help of six hurricane experts. The expert evaluation showed that the illustration-inspired techniques were preferred over existing tools. Visualization of the evolution of structural features is a prelude to a deeper visual analysis of the underlying dynamics.