Toward a fully integrated wireless wearable EEG-NIRS bimodal acquisition system.

OBJECTIVE: Interactions between neuronal electrical activity and regional changes in microcirculation are assumed to play a major role in physiological brain activity and the development of pathological disorders, but have been poorly elucidated to date. There is a need for advanced diagnostic tools to investigate the relationships between these two physiological processes. APPROACH: To meet these needs, a wireless wearable system has been developed, which combines a near infrared spectroscopy (NIRS) system using light emitting diodes (LEDs) as a light source and silicon photodiodes as a detector with an integrated electroencephalography (EEG) system. MAIN RESULTS: The main advantages over currently available devices are miniaturization and integration of a real-time electrical and hemodynamic activity monitor into one wearable device. For patient distributed monitoring and creating a body-area network, up to seven same devices can be connected to a single base station (PC) synchronously. Each node presents enhanced portability due to the wireless communication and highly integrated components resulting in a small, lightweight signal acquisition device. Further progress includes the individual control of LEDs output to automatically or interactively adjust emitted light to the actual local situation online, the use of silicon photodiodes with a safe low-voltage power supply, and an integrated three dimensional accelerometer for movement detection for the identification of motion artifacts. SIGNIFICANCE: The device was tested and validated using our enhanced EEG-NIRS tissue mimicking fluid phantom for sensitivity mapping. Typical somatotopic electrical evoked potential experiments were performed to verify clinical applicability.

A novel method for retinal vessel tracking using particle filters.

Extraction of a proper map from the vessel paths in the retinal images is a prerequisite for many applications such as identification. In this paper, we present a new approach based on particle filtering to determine and locally track the vessel paths in retina. Particle filter needs to use an acceptable probability density function (PDF) describing the blood vessels which must be provided by the retinal image. For this purpose, the product of the green and blue channels of the RGB retinal images is considered and after a median filtering stage, it is used as a PDF for tracking procedure. Then a stage of optic disc localization is performed to localize the starting points around the optic disc. With a proper set of starting points, the iterative tracking procedure initiates. First, a uniform propagation of the particles on an annular ring around each point (including starting points or ones determined as central points in the previous iteration) is performed. The particle weights are evaluated and accordingly, each particle is decided to be inside or outside the vessel. The subsequent stage is to analyze the hypothetical vectors between a central point and each of the inside vessel particles to find ones located inside vessel. Afterwards, the particles are clustered using quality threshold clustering method. Finally, each cluster introduces a central point for pursuing the tracking procedure in the next iteration. The tracking proceeds towards a bifurcation or the end of the vessels. We introduced two criteria: automatic/manually tracked ratio (AMTR) and false/manually tracked ratio (FMTR) for evaluating the tracking results. Apart from the labeling accuracy, the average values of AMTR and FMTR were 0.7746 and 0.2091, respectively. The proposed method successfully deals with the bifurcations with robustness against noise and tracks the thin vessels.

Skull segmentation in 3D neonatal MRI using hybrid Hopfield Neural Network.

A fully automated method for segmentation of neonatal skull in Magnetic Resonance (MR) images for source localization of electrical/magnetic encephalography (EEG/MEG) signals is proposed. Finding the source of these signals shows the origin of an abnormality. We propose a hybrid algorithm in which a Bayesian classifying framework is combined with a Hopfield Neural Network (HNN) for neonatal skull segmentation. Due to the non-homogeneity of skull intensities in MR images, local statistical parameters are used for adaptive training of Hopfield neural network based on Bayesian classifier error. The experimental results, which are obtained on high resolution T1-weighted MR images of nine neonates with gestational ages between 39 and 42 weeks, show 65% accuracy which consistently exhibits our scheme's superiority in comparison with previous neonatal skull segmentation methods.

Quantitative analysis of left ventricular performance from sequences of cardiac magnetic resonance imaging using active mesh model.

In this study, the local and global left ventricular function are estimated by fitting three-dimensional active mesh model (3D-AMM) to the initial sparse displacement which is measured from an establishing point correspondence procedure. To evaluate the performance of the algorithm, eight image sequences were used and the results were compared with those reported by other researchers. The findings were consistent with previously published values and the clinical evidence as well. The results demonstrated the superiority of the novel strategy with respect to formerly presented algorithm reported by author et al. Furthermore, the results are comparable to the current state-of-the-art methods.

Automatic detection of clustered microcalcifications in digital mammograms: Study on applying adaboost with SVM-based component classifiers.

This paper presents a computer-aided diagnosis (CAD) system for automatic detection of clustered microcalcifications (MCs) in digitized mammograms. The proposed system consists of two main steps. First, potential MC pixels in the mammograms are segmented out by using four mixed features consisting of two wavelet features and two gray level statistical features and then the potential MC pixels are labeled into potential individual MC objects by their spatial connectivity. Second, MCs are detected by extracting a set of 17 features from the potential individual MC objects. The classifier which is used in the first step is a multilayer feedforward neural network classifier but for the second step we have used Adaboost with SVM-based component classifier. Component classifiers which we used in our combining method are SVM classifiers with RBF kernel. The method was applied to a database of 40 mammograms (Nijmegen database) containing 105 clusters of MCs. A free-response operating characteristics (FROC) curve is used to evaluate the performance of CAD system. Results show that the proposed system gives quite satisfactory performance. In particular, 89.55% mean true positive detection rate is achieved at the cost of 0.921 false positive per image.

Neonatal probabilistic models for brain, CSF and skull using T1-MRI data: preliminary results.

Inappropriate results may be produced if one uses adult or pediatric atlases for evaluation of neonatal cerebral images for morphological studies. This is mainly due to anatomical particularities typical for this early stage of development. In this paper, we describe the construction of a digital neonatal brain atlas from a set of images of neonates aged between 39 and 42 weeks. It consists of probabilistic models for brain, cerebrospinal fluid (CSF) and skull. In the first step, the selected images are segmented automatically followed by manual correction. In the second step, the images are normalized to a stereotaxic space defined by the neonatal brain atlas template GRAMFC_T(39-42) using a popular normalization algorithm implemented in Statistical Parametric Mapping (SPM). The normalization parameters of individual subjects are then used to resample the corresponding brain, CSF and skull. Finally, to construct the probabilistic models, the average is computed for each voxel location. The atlas might be used for different applications such as source localization or neonatal structural image analysis.

Segmentation of scalp and skull in neonatal MR images using probabilistic atlas and level set method.

In this paper, we present a novel automatic algorithm for scalp and skull segmentation in T1-weighted neonatal head MR images. First, the probabilistic scalp and skull atlases are constructed. Second, the scalp outer surface is extracted based on an active mesh method. Third, maximum number of boundary points corresponding to the scalp inner surface is extracted using the constructed scalp probabilistic atlas and a set of knowledge based rules. In the next step, the skull inner surface and maximum number of boundary points of the outer surface are extracted using a priori information of the head anatomy and the constructed skull probabilistic atlas. Finally, the fast sweeping, tagging and level set methods are applied to reconstruct surfaces from the detected points in three-dimensional space. The results of the new segmentation algorithm on MRI data acquired from nine newborns (including three atlas and six test subjects) were compared with manual segmented data provided by an expert radiologist. The average similarity indices for the scalp and skull segmented regions were equal to 89% and 71% for the atlas and 84% and 63% for the test data, respectively.