Integrating semi-supervised label propagation and random forests for multi-atlas based hippocampus segmentation

A novel multi-atlas based image segmentation method is proposed by integrating a semi-supervised label propagation method and a supervised random forests method in a pattern recognition based label fusion framework. The semi-supervised label propagation method takes into consideration local and global image appearance of images to be segmented and segments the images by propagating reliable segmentation results obtained by the supervised random forests method. Particularly, the random forests method is used to train a regression model based on image patches of atlas images for each voxel of the images to be segmented. The regression model is used to obtain reliable segmentation results to guide the label propagation for the segmentation. The proposed method has been compared with state-of-the-art multi-atlas based image segmentation methods for segmenting the hippocampus in MR images. The experiment results have demonstrated that our method obtained superior segmentation performance.Comment: Accepted paper in IEEE International Symposium on Biomedical Imaging (ISBI), 201

Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data

Classification of ultrasound (US) kidney images for diagnosis of congenital abnormalities of the kidney and urinary tract (CAKUT) in children is a challenging task. It is desirable to improve existing pattern classification models that are built upon conventional image features. In this study, we propose a transfer learning-based method to extract imaging features from US kidney images in order to improve the CAKUT diagnosis in children. Particularly, a pre-trained deep learning model (imagenet-caffe-alex) is adopted for transfer learning-based feature extraction from 3-channel feature maps computed from US images, including original images, gradient features, and distanced transform features. Support vector machine classifiers are then built upon different sets of features, including the transfer learning features, conventional imaging features, and their combination. Experimental results have demonstrated that the combination of transfer learning features and conventional imaging features yielded the best classification performance for distinguishing CAKUT patients from normal controls based on their US kidney images.Comment: Accepted paper in IEEE International Symposium on Biomedical Imaging (ISBI), 201

Combine Target Extraction and Enhancement Methods to Fuse Infrared and LLL Images

For getting the useful object information from infrared image and mining more detail of low light level (LLL) image, we propose a new fusion method based on segmentation and enhancement methods in the paper. First, using 2D maximum entropy method to segment the original infrared image for extracting infrared target, enhancing original LLL image by Zadeh transform for mining more detail information, on the basis of the segmented map to fuse the enhanced LLL image and original infrared image. Then, original infrared image, the enhanced LLL image and the first fused image are used to realize fusion in non-subsampled contourlet transform (NSCT) domain, we get the second fused image. By contrast of experiments, the fused image of the second fused method’s visual effect is better than other methods’ from the literature. Finally, Objective evaluation is used to evaluate the fused images’ quality, its results also show that the proposed method can pop target information, improve fused image’s resolution and contrast

Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation

A novel label fusion method for multi-atlas based image segmentation method is developed by integrating semi-supervised and supervised machine learning techniques. Particularly, our method is developed in a pattern recognition based multi-atlas label fusion framework. We build random forests classification models for each image voxel to be segmented based on its corresponding image patches of atlas images that have been registered to the image to be segmented. The voxelwise random forests classification models are then applied to the image to be segmented to obtain a probabilistic segmentation map. Finally, a semi-supervised label propagation method is adapted to refine the probabilistic segmentation map by propagating its reliable voxelwise segmentation labels, taking into consideration consistency of local and global image appearance of the image to be segmented. The proposed method has been evaluated for segmenting the hippocampus in MR images and compared with alternative machine learning based multi-atlas based image segmentation methods. The experiment results have demonstrated that our method could obtain competitive segmentation performance (average Dice index > 0.88), compared with alternative multi-atlas based image segmentation methods under comparison. Source codes of the methods under comparison are publicly available at www.nitrc.org/frs/?group_id=1242

Hydrogen Generation from Al-NiCl2/NaBH4 Mixture Affected by Lanthanum Metal

The effect of La on Al/NaBH4 hydrolysis was elaborated in the present paper. Hydrogen generation amount increases but hydrogen generation rate decreases with La content increasing. There is an optimized composition that Al-15 wt% La-5 wt% NiCl2/NaBH4 mixture (Al-15 wt% La-5 wt% NiCl2/NaBH4 weight ratio, 1 : 3) has 126 mL g−1 min−1 maximum hydrogen generation rate and 1764 mL g−1 hydrogen generation amount within 60 min. The efficiency is 88%. Combined with NiCl2, La has great effect on NaBH4 hydrolysis but has little effect on Al hydrolysis. Increasing La content is helpful to decrease the particle size of Al-La-NiCl2 in the milling process, which induces that the hydrolysis byproduct Ni2B is highly distributed into Al(OH)3 and the catalytic reactivity of Ni2B/Al(OH)3 is increased therefore. But hydrolysis byproduct La(OH)3 deposits on Al surface and leads to some side effect. The Al-La-NiCl2/NaBH4 mixture has good stability in low temperature and its hydrolytic performance can be improved with increasing global temperature. Therefore, the mixture has good safety and can be applied as on board hydrogen generation material

Simultaneous separation and quantitative determination of monosaccharides, uronic acids, and aldonic acids by high performance anion-exchange chromatography coupled with pulsed amperometric detection in corn stover prehydrolysates

A method for simultaneous separation and quantitative determination of arabinose, galactose, glucose, xylose, xylonic acid, gluconic acid, galacturonic acid, and glucuronic acid was developed by using high performance anion-exchange chromatography coupled with pulsed amperometric detection (HPAEC-PAD). The separation was performed on a CarboPacTM PA-10 column (250 mm × 2 mm) with a various gradient elution of NaOH-NaOAc solution as the mobile phase. The calibration curves showed good linearity (R2 ≥ 0.9993) for the monosaccharides, uronic acids, and aldonic acids in the range of 0.1 to 12.5 mg/L. The detection limits (LODs) and the quantification limits (LOQs) were 4.91 to 18.75 μg/L and 16.36 to 62.50 μg/L, respectively. Relative standard deviations (RSDs) of the retention times and peak areas for the seven consecutive determinations of an unknown amount of mixture were 0.15% to 0.44% and 0.22% to 2.31%, respectively. The established method was used to separate and determine four monosaccharides, two uronic acids, and two aldonic acids in the prehydrolysate from dilute acid steam-exploded corn stover within 21 min. The spiked recoveries of monosaccharides, uronic acids, and aldonic acids ranged from 91.25% to 108.81%, with RSDs (n=3) of 0.04% ~ 6.07%. This method was applied to evaluate the quantitative variation of sugar and sugar acid content in biomass prehydrolysates

Probing Primordial Gravitational Waves: Ali CMB Polarization Telescope

In this paper, we will give a general introduction to the project of Ali CMB Polarization Telescope (AliCPT), which is a Sino-US joint project led by the Institute of High Energy Physics (IHEP) and has involved many different institutes in China. It is the first ground-based Cosmic Microwave Background (CMB) polarization experiment in China and an integral part of China's Gravitational Waves Program. The main scientific goal of AliCPT project is to probe the primordial gravitational waves (PGWs) originated from the very early Universe. The AliCPT project includes two stages. The first stage referred to as AliCPT-1, is to build a telescope in the Ali region of Tibet with an altitude of 5,250 meters. Once completed, it will be the worldwide highest ground-based CMB observatory and open a new window for probing PGWs in northern hemisphere. AliCPT-1 telescope is designed to have about 7,000 TES detectors at 90GHz and 150GHz. The second stage is to have a more sensitive telescope (AliCPT-2) with the number of detectors more than 20,000. Our simulations show that AliCPT will improve the current constraint on the tensor-to-scalar ratio $r$ by one order of magnitude with 3 years' observation. Besides the PGWs, the AliCPT will also enable a precise measurement on the CMB rotation angle and provide a precise test on the CPT symmetry. We show 3 years' observation will improve the current limit by two order of magnitude.Comment: 11 pages, 7 figures, 2 table