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Exploring the geometric prior in the dimensionality reduction (DR) of hyperspectral image data (HID) is an important issue because it can overcome the possible overclassification of spectrally homogeneous areas in the HID classification. In this paper, the local geometric similarity of hyperspectral vectors is explored in both the manifold domain and image domain, and a semisupervised dual-geometric subspace projection (DGSP) approach is proposed for the DR of HID, by utilizing both labeled and unlabeled samples. First, the geometric information in the manifold domain is captured by a sparse coding-based geometric graph, and then, a local-consistency-constrained geometric matrix is defined to reveal the geometric structure in the image domain. Second, unlabeled samples are used to refine the geometric structure by defining a pairwise similarity matrix. Third, three scatter matrices are then derived from these similarity matrices to find the optimal subspace projection that captures the most important properties of the subspaces with respect to classification. Some experiments are taken on the airborne visible infrared imaging spectrometer (AVIRIS) HID to prove the efficiency of the proposed method. [ABSTRACT FROM PUBLISHER]

The article presents a retrospective study on the microsurgical treatment of 174 intramedullary spinal cord tumors (IMSCTs). It notes that 48.9% of the tumors are ependymoma while total resection was possible for 60.9% of the patient-respondent. It reveals on the urgent treatment for most IMSCTs and the necessity of radiation therapy for malignant tumors and for those wherein total removal cannot be applied.

During the acquisition process hyperspectral images (HSI) are inevitably corrupted by various noises, which greatly influence their visual impression and subsequent applications. In this paper, a novel Bayesian approach integrating hierarchical sparse learning and spectral-spatial information is proposed for HSI denoising. Based on the structure correlations, spectral bands with similar and continuous features are segmented into the same band-subset. To exploit local similarity, each subset is then divided into overlapping cubic patches. All patches can be regarded as consisting of clean image component, Gaussian noise component and sparse noise component. The first term is depicted by a linear combination of dictionary elements, where Gaussian process with Gamma distribution is applied to impose spatial consistency on dictionary. The last two terms are utilized to fully depict the noise characteristics. Furthermore, the sparseness of the model is adaptively manifested through Beta-Bernoulli process. Calculated by Gibbs sampler, the proposed model can directly predict the noise and dictionary without priori information of the degraded HSI. The experimental results on both synthetic and real HSI demonstrate that the proposed approach can better suppress the existing various noises and preserve the structure/spectral-spatial information than the compared state-of-art approaches. [ABSTRACT FROM AUTHOR]

The aim of the present case study was to investigate the advantages of intraoperative magnetic resonance imaging (iMRI) on the real-time guidance and monitoring of a stereotactic biopsy. The study describes a patient with intracranial lesions, which were examined by conventional MRI and diffusion tensor imaging using a 1.5T intraoperative MRI system. The digital and pre-operative positron emission/computed tomography image data were transferred to a BrainLAB planning workstation, and a variety of images were automatically fused. The BrainLAB software was then used to reconstruct the corticospinal tract (CST) and create a three-dimensional display of the anatomical association between the CST and the brain lesions. A Leksell surgical planning workstation was used to identify the ideal target site and a reasonable needle track for the biopsy. The 1.5T iMRI was used to effectively monitor the intracranial condition during the brain biopsy procedure. Post-operatively, the original symptoms of the patient were not aggravated and no further neurological deficits were apparent. The histopathological diagnosis of non-Hodgkin's B-cell lymphoma was made. Using high-field iMRI, the multi-image fusion-guided stereotactic brain biopsy allows for a higher positive rate of biopsy and a lower incidence of complications. The approach of combining multi-image fusion images with the frame-based stereotactic biopsy may be clinically useful for intracranial lesions of deep functional areas. [ABSTRACT FROM AUTHOR]

Cynoglossus semilaevis blood biochemical parameters and antioxidant indexes that respond to fishing stress were measured under the influence of different feed taurine levels. With taurine levels of 0%, 0.5%, 1% and 1.5%, 4 groups of test feeds were prepared. After 28 days of feeding. Serum was collected before stress and at 0, 4, 12, 24, 48, 96 h after stress to determine the content of total protein, cholesterol, GPT, GOT, nitric oxide, lysozyme, blood sugar and the activity of glutathione (GSH-PX), superoxide dismutase (SOD), malondialdehyde (MDA) and antioxidant capacity (AOC), the results show: Feeding a feed with a taurine content of 0.5% has a better inhibitory effect on cholesterol and GOT in Cynoglossus semilaevis after fishing stress, and has obvious antistress effect on lysozyme and protein. The 0.5% group had the lowest MDA content 12 h after fishing stress. The results suggested that feeding the feed with a taurine content of 0.5%, the Cynoglossus semilaevis blood biochemical parameters and antioxidant indexes to fishing stress has a better response. [ABSTRACT FROM AUTHOR]

Panchromatic (PAN) and multispectral (MS) imagery classification is one of the hottest topics in the field of remote sensing. In recent years, deep learning techniques have been widely applied in many areas of image processing. In this paper, an end-to-end learning framework based on deep multiple instance learning (DMIL) is proposed for MS and PAN images' classification using the joint spectral and spatial information based on feature fusion. There are two instances in the proposed framework: one instance is used to capture the spatial information of PAN and the other is used to describe the spectral information of MS. The features obtained by the two instances are concatenated directly, which can be treated as simple fusion features. To fully fuse the spatial-spectral information for further classification, the simple fusion features are fed into a fusion network with three fully connected layers to learn the high-level fusion features. Classification experiments carried out on four different airborne MS and PAN images indicate that the classifier provides feasible and efficient solution. It demonstrates that DMIL performs better than using a convolutional neural network and a stacked autoencoder network separately. In addition, this paper shows that the DMIL model can learn and fuse spectral and spatial information effectively, and has huge potential for MS and PAN imagery classification. [ABSTRACT FROM AUTHOR]

Recently, saliency detection has been becoming a popular topic in computer vision. In this paper we propose an object-level saliency detection algorithm which explicitly explores bottom-up visual attention and objectness cues. Firstly, some category-independent object candidates are segmented from the image by the quantized color attributes. Then two metrics, global cues and candidate objectness, are developed to estimate the saliency in the whole image and the object candidates respectively. We use global cues to describe the focusness and spatial distribution of color attributes in the entire image. As supplementary, candidate objectness can reveal the objectness of the object candidates. Finally, we integrate the two saliency cues to derive a saliency map of the image. By explicitly fusing candidate objectness and global cues, our proposed method is more suitable for processing images with complex background. Experimental results on three large benchmark databases demonstrate that the proposed method achieves more favorable performance than 15 recent state-of-the-art methods in terms of three evaluation criterions. [ABSTRACT FROM AUTHOR]

A finite element mesh aggregating approach is presented to reconstruct images of multiple internal bioluminescence sources. Rather than assuming independence between mesh nodes, the proposed reconstruction strategy exploits spatial structure of nodes and aggregation feature of density distribution on the finite element mesh to adaptively determine the number of sources and to improve the quality of reconstructed images. With the proposed strategy integrated in the regularization-based reconstruction process, reconstruction algorithms need no a priori knowledge of source number; even more importantly, they can automatically reconstruct multiple sources that differ greatly in density or power. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer's disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy. [ABSTRACT FROM AUTHOR]