Influence of brain atrophy using semiquantitative analysis in [123I]FP-CIT single-photon emission computed tomography by a Monte Carlo simulation study

The specific binding ratio (SBR) is an objective indicator of N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-[123I] iodophenyl) nortropane ([123I]FP-CIT) single-photon emission computed tomography (SPECT) that could be used for the diagnosis of Parkinson’s disease and Lewy body dementia. One of the issues of the SBR analysis is that the setting position of the volume of interest (VOI) may contain cerebral ventricles and cerebral grooves. These areas may become prominent during the brain atrophy analysis; however, this phenomenon has not been evaluated enough. This study thus used Monte Carlo simulations to examine the effect of brain atrophy on the SBR analysis. The brain atrophy model (BAM) used to simulate the three stages of brain atrophy was made using a morphological operation. Brain atrophy levels were defined in the descending order from 1 to 3, with Level 3 indicating to the most severe damage. Projection data were created based on BAM, and the SPECT reconstruction was performed. The ratio of the striatal to background region accumulation was set to a rate of 8:1, 6:1, and 4:1. The striatal and the reference VOI mean value were decreased as brain atrophy progressed. Additionally, the Bolt’s analysis methods revealed that the reference VOI value was more affected by brain atrophy than the striatal VOI value. Finally, the calculated SBR value was overestimated as brain atrophy progressed, and a similar trend was observed when the ratios of the striatal to background region accumulation were changed. This study thus suggests that the SBR can be overestimated in cases of advanced brain atrophy.

Research on 3D crack segmentation of CT images of oil rock core

In this paper, we propose a framework for CT image segmentation of oil rock core. According to the characteristics of CT image of oil rock core, the existing level set segmentation algorithm is improved. Firstly, an algorithm of Chan-Vese (C-V) model is carried out to segment rock core from image background. Secondly the gray level of image background region is replaced by the average gray level of rock core, so that image background does not affect the binary segmentation. Next, median filtering processing is carried out. Finally, an algorithm of local binary fitting (LBF) model is executed to obtain the crack region. The proposed algorithm has been applied to oil rock core CT images with promising results.

A Machine Learning Method Based on 3D Local Surface Representation for Recognizing the Inscriptions on Ancient Stele

It is challenging to extract reliefs from ancient steles due to their rough surfaces which contain relief-like noise such as dents and scratches. In this paper, we propose a method to segment relief region from 3D scanned ancient stele by exploiting local surface characteristics. For each surface point, four points that are apart from the reference point along the direction of the principal curvatures of the point are identified. The spin images of the reference point and the four relative points are concatenated to provide additional local surface information of the reference point. A random forest model is trained with the local surface features and thereafter, used to classify 3D surface point as relief or non-relief. To effectively distinguish relief from the degraded surface region containing relief-like noise, the model is trained using three-class labels consisting of relief, background, and degraded surface region. The initial three-class result obtained from the model is refined using the k-nearest neighbors algorithm, and finally, the degraded region is re-labeled to background region. Experimental results show that the proposed method performed better than the state-of-the-art, SVM-based method with a margin of 0.68%, 3.53%, 2.25%, and 2.36%, in accuracy, precision, F1 score, and SIRI, respectively. When compared with the height- and curvature-based methods, the proposed method outperforms these existing methods with an accuracy, precision, F1 score, and SIRI of over 4%, 20%, 11%, and 12%, respectively.

A Foreground-Aware Framework for Local Face Attribute Transfer

In the context of social media, large amounts of headshot photos are taken everyday. Unfortunately, in addition to laborious editing and modification, creating a visually compelling photographic masterpiece for sharing requires advanced professional skills, which are difficult for ordinary Internet users. Though there are many algorithms automatically and globally transferring the style from one image to another, they fail to respect the semantics of the scene and are unable to allow users to merely transfer the attributes of one or two face organs in the foreground region leaving the background region unchanged. To overcome this problem, we developed a novel framework for semantically meaningful local face attribute transfer, which can flexibly transfer the local attribute of a face organ from the reference image to a semantically equivalent organ in the input image, while preserving the background. Our method involves warping the reference photo to match the shape, pose, location, and expression of the input image. The fusion of the warped reference image and input image is then taken as the initialized image for a neural style transfer algorithm. Our method achieves better performance in terms of inception score (3.81) and Fréchet inception distance (80.31), which is about 10% higher than those of competitors, indicating that our framework is capable of producing high-quality and photorealistic attribute transfer results. Both theoretical findings and experimental results are provided to demonstrate the efficacy of the proposed framework, reveal its superiority over other state-of-the-art alternatives.

Submicron Aerosol and Black Carbon in the Troposphere of Southwestern Siberia (1997–2018)

Based on the multiyear measurements in the surface atmospheric layer (from five stations) and regular flights of aircraft laboratory over the background region of Southwestern Siberia, the compositions of mass concentrations of submicron aerosol and absorbing substances (soot and black carbon) are analyzed. The annual average concentrations of submicron aerosol and black carbon were found to be maximal in 1997, 2012, and 2016, when the largest numbers of wildfires occurred across the entire territory of Siberia. No significant, unidirectional trend of interannual variations in the concentration of submicron particles was observed, while the concentration of absorbing substance reliably decreased by 1.5% each year. To estimate the effect of urban pollutants, mass concentrations of aerosol and absorbing substance in the surface layer at the Aerosol Station (in the suburban region of Tomsk) were compared to those at the Fonovaya Observatory (in the background region). It was shown that the largest contribution of anthropogenic sources in the suburban region was observed in the winter season, while minimal difference was observed in the warm period of the year. The seasonal behavior of the concentrations of elemental carbon at three stations in Novosibirsk Oblast almost completely matched the dynamics of the variations in the black carbon concentration in the atmosphere of Tomsk Oblast. Data of aircraft sensing in the troposphere of the background region of Southwestern Siberia (2000–2018) were used to determine the average values of the vertical distribution of the submicron aerosol and black carbon concentrations in the altitude range of 0.5–7 km for each season. It was found that at altitudes of 0.5–7 km, there were no unidirectional trends in submicron aerosol; however, there was an increase of black carbon concentration at all altitudes with a positive trend of 5.3 ± 2.2% per year at an altitude of 1.5 km, significant at a p-value = 0.05.

Fast Segmentation Method for Sonar Images Blurred by Noise Reduction

It has remained a hard nut for years to segment sonar images, most of which are noisy images with inevitable blur after noise reduction. For the purpose of solutions to this problem, a fast segmentation algorithm is proposed on the basis of the gray value characteristics of sonar images. This algorithm is endowed with the advantage in no need of segmentation thresholds to be calculated. To realize this goal, it follows the undermentioned steps: first, calculate the gray matrix of the fuzzy image background. After adjusting the gray value, segment the region into the background region, buffer region and target regions. After filtering, reset the pixels with gray value lower than 255 to binarize images and eliminate most artifacts. Finally, remove the remaining noise from images by means of morphological image processing. The simulation results of several sonar images show that the algorithm can segment the fuzzy sonar image quickly and effectively, with no problem of incomplete image target shape. Thus, the stable and feasible method is testified.