Lumen Segmentation in Intravascular Optical Coherence Tomography Using Backscattering Tracked and Initialized Random Walks
In this paper, we propose a framework for automatically segmenting the lumen boundary in IV-OCT with minimum time‐complexity. The framework uses OCT imaging physics based graph representation of signals and random walks image segmentation approaches. First, each OCT frame is modelled as a 4-connected graph and edge weights are assigned incorporating OCT signal attenuation physics models. Second, optical backscattering maxima is tracked along each A-scan of OCT and is subsequently refined using global gray‐level statistics and used for initializing seeds. This automates the seed selection process thus avoiding manual interaction. Finally, lumen boundary is segmented using the random walks image segmentation using the initialized seeds.
Skull Segmentation and Reconstruction From Newborn CT Images Using Coupled Level Sets
A new framework based on variational coupled level set has been developed for the extraction of new born skull including fontanels and sutures from CT images. The proposed method utilizes hard tissue contrast in CT image, prior information of head shape integrated in level sets initialization, and a predefined constraint to impose surface reconstruction properties. The proposed method was evaluated using eighteen neonatal CT images. The segmentation results achieved by the suggested method have been compared with manual segmentations.
Improvement in Neural Respiratory Drive Estimation From Diaphragm Electromyographic Signals Using Fixed Sample Entropy
In this work, surface EMGdi signals were analyzed using the fixed sample entropy (fSampEn) algorithm, and compared to traditional ARV and RMS methods. The fSampEn is calculated using a tolerance value fixed and independent of the standard deviation of the analysis window. Thus, this method quantifies the amplitude of the complex components of stochastic signals (such as EMGdi), and being less affected by changes in amplitude due to less complex components (such as ECG).
Special Section: Biomedical and Health Informatics for Diabetes
In this special issue, we have compiled eight papers from those submitted to the JBHI call for papers on “Biomedical and Health Informatics for Diabetes” and those through normal submission route. The final set of papers includes a broad range of topics covering areas of technologies and informatics that are keys to the management of diabetes…
Multimodality Neurological Data Visualization with Multi-VOI Based DTI Fiber Dynamic Integration
Brain lesions are usually located adjacent to critical spinal structures, so it will be a challenging task for neurosurgeons to precisely plan a surgical procedure without damaging healthy tissues and nerves. The advancement of medical imaging technologies produces a large amount of neurological data, which are capable of showing a wide variety of brain properties. In this paper, we describe new algorithms and a software framework for multiple volume of interest specified diffusion tensor imaging (DTI) fiber dynamic visualization…
Unobtrusive Monitoring of Neonatal Brain Temperature Using a Zero-Heat-Flux Sensor Matrix
The temperature of preterm neonates must be maintained within a narrow window to ensure their survival. Continuously measuring their core temperature provides an optimal means of monitoring their thermoregulation and their response to environmental changes. This work investigates an unobtrusive method of measuring brain temperature continuously using an embedded zero-heat-flux (ZHF) sensor matrix placed under the head of the neonate…