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In this paper, a fetal electrocardiogram (ECG) monitoring system based on the Android smartphone was proposed. We designed a portable low-power fetal ECG collector, which collected maternal abdominal ECG signals in real time. The ECG data were sent to a smartphone client via Bluetooth. Smartphone app software was developed based on the Android system. The app integrated the fast fixed-point algorithm for independent component analysis (FastICA) and the sample entropy algorithm, for the sake of real-time extraction of fetal ECG signals from the maternal abdominal ECG signals. The fetal heart rate was computed using the extracted fetal ECG signals. Experimental results showed that the FastICA algorithm can extract a clear fetal ECG, and the sample entropy can correctly determine the channel where the fetal ECG is located. The proposed fetal ECG monitoring system may be feasible for non-invasive, real-time monitoring of fetal ECGs., Competing Interests: The authors declare no conflicts of interest.

The architecture and parameter initialization of wavelet neural network are discussed and a novel initialization method is proposed. The new approach can be regarded as a dynamic clustering procedure which will derive the neuron number as well as the initial value of translation and dilation parameters according to the input patterns and the activating wavelets functions. Three simulation examples are given to examine the performance of our method as well as Zhang's heuristic initialization approach. The results show that the new approach not only can decide the WNN structure automatically, but also provides superior initial parameter values that make the optimization process more stable and quickly.

Background and Purpose: This study aims to investigate the regional changes in the early onset of blindness using the deformation-based morphometry (DBM) method., Methods: A total of 15 early blindness and 30 gender- and age-matched sighted control subjects were recruited for the study. Statistically significant changes in regional volume were analyzed using DBM through HAMMER registration. Using this technique, changes in regional tissue volume can be detected on the basis of the deformation field derived from the warping subject to the template image. A voxelwise estimation of the morphological differences between the two groups can be acquired after applying a threshold (P < .001, uncorrected) to the statistic maps., Results: Regions with enlarged volume in the brains of blind subjects are mainly localized in the left associated visual cortex, posterior cingulated cortex, and cerebellum, whereas volume reductions are primarily localized in the left early visual cortex., Conclusions: DBM is an effective method for detecting entire brain structural changes in blindness. Visual deprivation actually alters the local structural organization during the early critical periods of neurodevelopment. Volume increases outside the occipital lobe detected with DBM may suggest compensatory adaptations., (Copyright © 2012 by the American Society of Neuroimaging.)

Due to the complex underwater environment and the selective absorption and scattering effect of water on light waves, underwater images often suffer from issues such as low contrast, color distortion, and blurred details. This paper presents a stable and effective algorithm for enhancing underwater images to address these challenges. Firstly, an improved color correction algorithm based on the gray world and minimum information loss is employed to remove the blue-green bias present in the images. Secondly, a contrast enhancement algorithm is based on the guided filter and wavelet decomposition to make the texture details of the image clearer. Then, the normalized weight map of the image is obtained to carry out multi-scale fusion. Finally, the fused image is applied to perform the multi-scale decomposition. The experimental results show that the algorithm proposed in this paper can correct the image color deviation, improve the image contrast and enhance the image details. [ABSTRACT FROM AUTHOR]

A novel breast ultrasound tomography system based on a circular array of capacitive micromechanical ultrasound transducers (CMUT) has broad application prospects. However, the images produced by this system are not suitable as input for the training phase of the super-resolution (SR) reconstruction algorithm. To solve the problem, this paper proposes an improved medical image super-resolution (MeSR) method based on the sparse domain. First, we use the simultaneous algebraic reconstruction technique (SART) with high imaging accuracy to reconstruct the image into a training image in a sparse domain model. Secondly, we denoise and enhance the contrast of the SART images to obtain improved detail images before training the dictionary. Then, we use the original detail image as the guide image to further process the improved detail image. Therefore, a high-precision dictionary was obtained during the testing phase and applied to filtered back projection SR reconstruction. We compared the proposed algorithm with previously reported algorithms in the Shepp Logan model and the model based on the CMUT background. The results showed significant improvements in peak signal-to-noise ratio, entropy, and average gradient compared to previously reported algorithms. The experimental results demonstrated that the proposed MeSR method can use noisy reconstructed images as input for the training phase of the SR algorithm and produce excellent visual effects. [ABSTRACT FROM AUTHOR]

The global k-means algorithm is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure from suitable initial positions, and employs k-means to minimize the sum of the intra-cluster variances. However the global k-means algorithm sometimes results singleton clusters and the initial positions sometimes are bad, after a bad initialization, poor local optimal can be easily obtained by k-means algorithm. In this paper, we modified the global k-means algorithm to eliminate the singleton clusters at first, and then we apply MinMax k-means clustering error method to global k-means algorithm to overcome the effect of bad initialization, proposed the global Minmax k-means algorithm. The proposed clustering method is tested on some popular data sets and compared to the k-means algorithm, the global k-means algorithm and the MinMax k-means algorithm. The experiment results show our proposed algorithm outperforms other algorithms mentioned in the paper. [ABSTRACT FROM AUTHOR]