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Purpose: To develop an automatic deep feature classification (DFC) method for distinguishing benign angiomyolipoma without visible fat (AMLwvf) from malignant clear cell renal cell carcinoma (ccRCC) from abdominal contrast-enhanced computer tomography (CE CT) images., Methods: A dataset including 80 abdominal CT images of 39 AMLwvf and 41 ccRCC patients was used. We proposed a DFC method for differentiating the small renal masses (SRM) into AMLwvf and ccRCC using the combination of hand-crafted and deep features, and machine learning classifiers. First, 71-dimensional hand-crafted features (HCF) of texture and shape were extracted from the SRM contours. Second, 1000-4000-dimensional deep features (DF) were extracted from the ImageNet pretrained deep learning model with the SRM image patches. In DF extraction, we proposed the texture image patches (TIP) to emphasize the texture information inside the mass in DFs and reduce the mass size variability. Finally, the two features were concatenated and the random forest (RF) classifier was trained on these concatenated features to classify the types of SRMs. The proposed method was tested on our dataset using leave-one-out cross-validation and evaluated using accuracy, sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and area under receiver operating characteristics curve (AUC). In experiments, the combinations of four deep learning models, AlexNet, VGGNet, GoogleNet, and ResNet, and four input image patches, including original, masked, mass-size, and texture image patches, were compared and analyzed., Results: In qualitative evaluation, we observed the change in feature distributions between the proposed and comparative methods using tSNE method. In quantitative evaluation, we evaluated and compared the classification results, and observed that (a) the proposed HCF + DF outperformed HCF-only and DF-only, (b) AlexNet showed generally the best performances among the CNN models, and (c) the proposed TIPs not only achieved the competitive performances among the input patches, but also steady performance regardless of CNN models. As a result, the proposed method achieved the accuracy of 76.6 ± 1.4% for the proposed HCF + DF with AlexNet and TIPs, which improved the accuracy by 6.6%p and 8.3%p compared to HCF-only and DF-only, respectively., Conclusions: The proposed shape features and TIPs improved the HCFs and DFs, respectively, and the feature concatenation further enhanced the quality of features for differentiating AMLwvf from ccRCC in abdominal CE CT images., (© 2018 American Association of Physicists in Medicine.)

This paper proposes the automatic coast mode tracking of centroid trackers for infrared images to overcome the target occlusion status. The centroid tracking method, using only the brightness information of an image, is still widely used in infrared imaging tracking systems because it is difficult to extract meaningful features from infrared images. However, centroid trackers are likely to lose the track because they are highly vulnerable to screened status by the clutter or background. Coast mode, one of the tracking modes, maintains the servo slew rate with the tracking rate right before the loss of track. The proposed automatic coast mode tracking method makes decisions regarding entering coast mode by the prediction of target occlusion and tries to re-lock the target and resume the tracking after blind time. This algorithm comprises three steps. The first step is the prediction process of the occlusion by checking both matters which have target-likelihood brightness and which may screen the target despite different brightness. The second step is the process making inertial tracking commands to the servo. The last step is the process of re-locking a target based on the target modeling of histogram ratio. The effectiveness of the proposed algorithm is addressed by presenting experimental results based on computer simulation with various test imagery sequences compared to published tracking algorithms. The proposed algorithm is tested under a real environment with a naval electro-optical tracking system (EOTS) and airborne EO/IR system., Competing Interests: The authors declare no conflict of interest.

The approximate nearest neighbor (ANN) search problem is important in applications such as information retrieval. Several hashing-based search methods that provide effective solutions to the ANN search problem have been proposed. However, most of these focus on similarity preservation and coding error minimization, and pay little attention to optimizing the precision-recall curve or receiver operating characteristic curve. In this paper, we propose a novel projection-based hashing method that attempts to maximize precision and recall. We first introduce an uncorrelated component analysis (UCA) transformation by examining precision and recall, and then propose a UCA-based hashing method. The proposed method is evaluated with a variety of data sets. The results show that UCA-based hashing outperforms state-of-the-art methods, and has computationally efficient training and encoding processes.

Societies in the 21st century require more proactive management of risks in many areas owing to the complexities people have built into socioeconomic systems, ranging from science and technology, economics, and finance to education, entertainment, and tourism infrastructure. All these systems thrive on stability, which can easily be challenged by unexpected risks. This study compares zooanthroponosis and climate change-induced sea level rise through a cost-benefit analysis in Korea. Borrowing from other methodological approaches, this study shows that the cost-benefit estimation is consistent with the existing macroeconomic speculations, which assume potential loss of GDP due to the two risks. This paper also presents a policy alternative of creating research institutions specializing in the two risks from a cost-benefit perspective as a prerequisite for the "Creative Economy"., (Copyright © 2014 Elsevier Inc. All rights reserved.)

We propose a graph-cut-based segmentation method for the anterior cruciate ligament (ACL) in knee MRI with a novel shape prior and label refinement. As the initial seeds for graph cuts, candidates for the ACL and the background are extracted from knee MRI roughly by means of adaptive thresholding with Gaussian mixture model fitting. The extracted ACL candidate is segmented iteratively by graph cuts with patient-specific shape constraints. Two shape constraints termed fence and neighbor costs are suggested such that the graph cuts prevent any leakage into adjacent regions with similar intensity. The segmented ACL label is refined by means of superpixel classification. Superpixel classification makes the segmented label propagate into missing inhomogeneous regions inside the ACL. In the experiments, the proposed method segmented the ACL with Dice similarity coefficient of 66.47±7.97%, average surface distance of 2.247±0.869, and root mean squared error of 3.538±1.633, which increased the accuracy by 14.8%, 40.3%, and 37.6% from the Boykov model, respectively., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The aryl hydrocarbon receptor (AhR) has become increasingly recognized for its role in the differentiation and activity of immune cell subsets; however, its role in regulating the activity of natural killer (NK) cells has not been described. Here, we show that AhR expression is induced in murine NK cells upon cytokine stimulation. We show that in the absence of AhR, NK cells have reduced cytolytic activity and reduced capacity to control RMA-S tumor formation in vivo, despite having normal development and maturation markers. Although AhR was first identified to bind the xenobiotic compound dioxin, AhR is now known to bind a variety of natural exogenous (e.g., dietary) and endogenous ligands. We show that activation of AhR with an endogenous tryptophan derivative, 6-formylindolo[3,2-b]carbazole, potentiates NK cell IFN-γ production and cytolytic activity. Further, administration of 6-formylindolo[3,2-b]carbazole in vivo enhances NK cell control of tumors in an NK cell- and AhR-dependent manner. Finally, similar effects on NK cell potency occur with AhR dietary ligands, potentially explaining the numerous associations that have been observed in the past between diet and NK cell function. Our studies introduce AhR as another regulator of NK cell activity in vivo.

In this paper, we present a new information-theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We assume that the probability densities associated with the image pixel intensities within each region are completely unknown a priori, and we formulate the problem based on nonparametric density estimates. Due to the nonparametric structure, our method does not require the image regions to have a particular type of probability distribution and does not require the extraction and use of a particular statistic. We solve the information-theoretic optimization problem by deriving the associated gradient flows and applying curve evolution techniques. We use level-set methods to implement the resulting evolution. The experimental results based on both synthetic and real images demonstrate that the proposed technique can solve a variety of challenging image segmentation problems. Futhermore, our method, which does not require any training, performs as good as methods based on training.