nils-herrmann/clean_scirepeval huggingface dataset:
Joint Probabilistic Model of Shape and Intensity for Multiple Abdominal Organ Segmentation From Volumetric CT Images

We propose a novel joint probabilistic model that correlates a new probabilistic shape model with the corresponding global intensity distribution to segment multiple abdominal organs simultaneously. Our probabilistic shape model estimates the probability of an individual voxel belonging to the estimated shape of the object. The probability density of the estimated shape is derived from a combination of the shape variations of target class and the observed shape information. To better capture the shape variations, we used probabilistic principle component analysis optimized by expectation maximization to capture the shape variations and reduce computational complexity. The maximum a posteriori estimation was optimized by the iterated conditional mode-expectation maximization. We used 72 training datasets including low- and high-contrast computed tomography images to construct the shape models for the liver, spleen, and both kidneys. We evaluated our algorithm on 40 test datasets that were grouped into normal (34 normal cases) and pathologic (six datasets) classes. The testing datasets were from different databases and manual segmentation was performed by different clinicians. We measured the volumetric overlap percentage error, relative volume difference, average square symmetric surface distance, false positive rate, and false negative rate and our method achieved accurate and robust segmentation for multiple abdominal organs simultaneously.
Morphology-based multifractal estimation for texture segmentation

Multifractal analysis is becoming more and more popular in image segmentation community, in which the box-counting based multifractal dimension estimations are most commonly used. However, in spite of its computational efficiency, the regular partition scheme used by various box-counting methods intrinsically produces less accurate results. In this paper, a novel multifractal estimation algorithm based on mathematical morphology is proposed and a set of new multifractal descriptors, namely the local morphological multifractal exponents is defined to characterize the local scaling properties of textures. A series of cubic structure elements and an iterative dilation scheme are utilized so that the computational complexity of the morphological operations can be tremendously reduced. Both the proposed algorithm and the box-counting based methods have been applied to the segmentation of texture mosaics and real images. The comparison results demonstrate that the morphological multifractal estimation can differentiate texture images more effectively and provide more robust segmentations.
A Multistage Discriminative Model for Tumor and Lymph Node Detection in Thoracic Images

Analysis of primary lung tumors and disease in regional lymph nodes is important for lung cancer staging, and an automated system that can detect both types of abnormalities will be helpful for clinical routine. In this paper, we present a new method to automatically detect both tumors and abnormal lymph nodes simultaneously from positron emission tomography-computed tomography thoracic images. We perform the detection in a multistage approach, by first detecting all potential abnormalities, then differentiate between tumors and lymph nodes, and finally refine the detected tumors for false positive reduction. Each stage is designed with a discriminative model based on support vector machines and conditional random fields, exploiting intensity, spatial and contextual features. The method is designed to handle a wide and complex variety of abnormal patterns found in clinical datasets, consisting of different spatial contexts of tumors and abnormal lymph nodes. We evaluated the proposed method thoroughly on clinical datasets, and encouraging results were obtained.
Bone texture characterization with fisher encoding of local descriptors

Bone texture characterization is important for differentiating osteoporotic and healthy subjects. Automated classification is however very challenging due to the high degree of visual similarity between the two types of images. In this paper, we propose to describe the bone textures by extracting dense sets of local descriptors and encoding them with the improved Fisher vector (IFV). Compared to the standard bag-of-visual-words (BoW) model, Fisher encoding is more discriminative by representing the distribution of local descriptors in addition to the occurrence frequencies. Our method is evaluated on the ISBI 2014 challenge dataset of bone texture characterization, and we demonstrate excellent classification performance compared to the challenge entries and large improvement over the BoW model.
Multiple scattering model based single image dehazing

Single image dehazing has been a challenging problem due to its ill-posed nature. Most of the existing dehazing algorithms are based on single atmospheric scattering model. However, this model is not capable of explaining complex effects of weather on images as multiple scattering occurs in real world. Existing multiple scattering models are widely utilized for simulating various weather condition; however, only a few algorithms are applied for dehazing. In this paper, we propose an image degradation model based on multiple scattering for image haze removal. We improve the widely utilized dichromatic model with atmospheric point spread function. Applying deconvolution to the dehazed image with the estimated blur kernel, the inherent scene radiance is restored. Experimental results with a benchmark dataset demonstrate that our approach significantly improves the contrast and sharpness of the hazy image.
Video Object Segmentation with Occlusion Map

Extracting foreground objects from a video captured by a hand-held camera has been a new challenge in video segmentation, since most of the existing approaches generally work well provided that certain assumptions on the background scene or camera motion (e.g. still surveillance cameras) are imposed. While some approaches exploit several clues such as depth and motion to extract the foreground layer from handheld camera videos, we propose to leverage the advances in high quality interactive image segmentation. That is, we treat each video frame as an individual image and segment foreground objects with interactive image segmentation algorithms. In order to simulate user interactions, we derive reliable occlusion map for foreground objects and use the occlusion map as the "seeding" interactive input to an interactive image segmentation approach. In this paper, we employ an optical flow based occlusion detection approach for extracting the occlusion map and Geodesic star convexity based interactive image segmentation approach. In order to obtain accurate "seeding" user interactions, both forward and backward occlusion maps are computed and utilized. As a result, our approach is able to extract the whole objects having only partial movements, which overcomes the limitation of the stateof- the-art algorithm. Experimental results demonstrate both the effectiveness and efficiency of our proposed approach.
Semi-supervised manifold learning with affinity regularization for Alzheimer's disease identification using positron emission tomography imaging

Dementia, Alzheimer's disease (AD) in particular is a global problem and big threat to the aging population. An image based computer-aided dementia diagnosis method is needed to providing doctors help during medical image examination. Many machine learning based dementia classification methods using medical imaging have been proposed and most of them achieve accurate results. However, most of these methods make use of supervised learning requiring fully labeled image dataset, which usually is not practical in real clinical environment. Using large amount of unlabeled images can improve the dementia classification performance. In this study we propose a new semi-supervised dementia classification method based on random manifold learning with affinity regularization. Three groups of spatial features are extracted from positron emission tomography (PET) images to construct an unsupervised random forest which is then used to regularize the manifold learning objective function. The proposed method, stat-of-the-art Laplacian support vector machine (LapSVM) and supervised SVM are applied to classify AD and normal controls (NC). The experiment results show that learning with unlabeled images indeed improves the classification performance. And our method outperforms LapSVM on the same dataset.
2013 IEEE Scientific Visualization Contest Winner: Observing Genomics and Phenotypical Patterns in the Developing Mouse Brain

The 2013  Visualization Contest focused on developmental neuroscience related to the mouse brain. In developmental neuroscience, researchers examine spatiotemporal patterns of gene expression to understand how brain structures evolve and vary over an organism's life. The Allen Developing Mouse Brain Atlas project generated expression data for thousands of genes with high spatial resolution over multiple developmental stages. These data have become an invaluable resource for neuroscientists and developmental biologists exploring interesting spatial and temporal patterns of gene expression. Given the extremely large amounts of data, it's desirable to apply visualization techniques to their access, analysis, and interpretation. The proposed visual-analytics system enables clear, insightful observations of the gene expression patterns for such data, focusing on structural patterns that vary across gene expression levels over six developmental stages. Users can explore patterns through specialized data-driven representations that capture emerging structure-function associations in the developing mouse brain. The system also provides an effective characterization of certain processes in the developing brain that could lead to further analysis.
Pairwise Latent Semantic Association for Similarity Computation in Medical Imaging

Retrieving medical images that present similar diseases is an active research area for diagnostics and therapy. However, it can be problematic given the visual variations between anatomical structures. In this paper, we propose a new feature extraction method for similarity computation in medical imaging. Instead of the low-level visual appearance, we design a CCA-PairLDA feature representation method to capture the similarity between images with high-level semantics. First, we extract the PairLDA topics to represent an image as a mixture of latent semantic topics in an image pair context. Second, we generate a CCA-correlation model to represent the semantic association between an image pair for similarity computation. While PairLDA adjusts the latent topics for all image pairs, CCA-correlation helps to associate an individual image pair. In this way, the semantic descriptions of an image pair are closely correlated, and naturally correspond to similarity computation between images. We evaluated our method on two public medical imaging datasets for image retrieval and showed improved performance.
Fusing subcategory probabilities for texture classification

Texture, as a fundamental characteristic of objects, has attracted much attention in computer vision research. Performance of texture classification is however still lacking for some challenging cases, largely due to the high intra-class variation and low inter-class distinction. To tackle these issues, in this paper, we propose a sub-categorization model for texture classification. By clustering each class into subcategories, classification probabilities at the subcategory-level are computed based on between-subcategory distinctiveness and within-subcategory representativeness. These subcategory probabilities are then fused based on their contribution levels and cluster qualities. This fused probability is added to the multiclass classification probability to obtain the final class label. Our method was applied to texture classification on three challenging datasets - KTH-TIPS2, FMD and DTD, and has shown excellent performance in comparison with the state-of-the-art approaches.
Layered-based exposure fusion algorithm

Owing to the limitation of dynamic range, a single still image is usually insufficient to describe a high contrast scene. Fusing multi-exposure images of the same scene can produce a resulting image with details both in the bright and the dark regions. However, they may be sensitive to the exposure parameters of the input images. In this study, a global layer is introduced to improve the robustness of the fusion method. The global layer is employed to preserve the overall luminance of a real scene and avoid possible luminance reversion artefacts. Then, details are recovered in the gradient domain by a Poisson solver. Experimental results show the superior performance of our approach in terms of robustness and details preservation.
Reliable object recognition using SIFT features

SIFT (Scale Invariant Feature Transform) features have been one of the most efficient descriptors for object recognition. However, the excessive number of key points and high dimensionality has limited its capacity in object recognition. In this paper we present a novel method based on SIFT features for reliable object recognition. At first, a matching tree is constructed to eliminate non-essential key points. In order to achieve viewpoint independence, a 3D model is constructed for each object in the filtered SIFT feature space. Experimental results on both Caltech 101 and COIL 100 datasets indicate the effectiveness of our proposed algorithm.
N-type Modulation-Doped InGaAlAs/InP Strain-Balanced Quantum-Well SOA/Laser Fabricated by a Multi-Step Wet-Etching Process

We report the design and MBE growth of strain-balanced epitaxial structures on InP containing n-type modulation-doped InGaAlAs/InGaAs quantum wells and a new multi-step wet-etching process for laser fabrication.
Graph-based energy-minimization segmentation and PCA applied to internal carotid extraction in neurological PET

An unseeded, graph-theoretic segmentation algorithm based on Mumford-Shah energy minimization is applied to segmentation of brain FDG dynamic positron emission tomography data after preprocessing by principal component analysis, for the automated extraction of regions of interest, and, in particular, extraction of the internal carotid arteries and venous sinuses for the noninvasive estimation of the input plasma time activity curve. Evaluation on clinical FDG brain PET studies show that the internal carotids and venous sinuses can be robustly segmented in typical dynamic PET data sets, allowing for the fully automatic estimation of the arterial input curve.
Localized Sparse Code Gradient in Alzheimer's disease staging

The accurate diagnosis of Alzheimer's disease (AD) at different stages is essential to identify patients at high risk of dementia and plan prevention or treatment measures accordingly. In this study, we proposed a new AD staging method for the entire spectrum of AD including the AD, Mild Cognitive Impairment with and without AD conversions, and Cognitive Normal groups. Our method embedded the high dimensional multi-view features derived from neuroimaging data into a low dimensional feature space and could form a more distinctive representation than the naive concatenated features. It also updated the testing data based on the Localized Sparse Code Gradients (LSCG) to further enhance the classification. The LSCG algorithm, validated using Magnetic Resonance Imaging data from the ADNI baseline cohort, achieved significant improvements on all diagnosis groups compared to using the original sparse coding method.
Automated and Robust PERCIST-based Thresholding framework for whole body PET-CT studies

Positron emission tomography (PET) is unique for quantitatively assessing treatment response before marked morphological changes are detectable by Computed Tomography (CT). PET response criterion (PERCIST) is a widely accepted approach of assessing metabolic response of malignant lesions by using Standardized uptake value (SUV) normalized by lean body mass (LBM) with a volume of interest (VOI) reference defined in the right lobe of liver. However, the operator-dependent delineation of VOI reference is a time consuming and subjective task. Although the VOI reference can be estimated from the co-aligned CT, the low-dose CT data in PET-CT poses challenge in liver segmentation. In this study, we propose a fully automatic framework to calculate the PERCIST-based thresholding for whole-body PET-CT studies. The framework consists of multi-atlas registration and voxel classification for CT data to segment liver structure and delineate the VOI reference, which is then mapped to the PET data to derive the value of SUVLBM thresholding for PET to select regions of high metabolism. We evaluated our framework with 28 clinical studies diagnosed with lung cancer or lymphoma, and demonstrated both reliability and efficiency in depicting lesions using PERCIST thresholding.
Novel Stepped-Width Design Concept for Compact Multimode-Interference Couplers With Low Cross-Coupling Ratio

We show through detailed simulations that the length of conventionally designed multimode-interference couplers for low (<50%) cross-coupling ratios can be shortened significantly by replacing a segment that is 3/2-times the coupling length by a minimum-width design. For the 15% cross-coupling device, this stepped-width design results in 32% length reduction. With additional incorporation of adiabatic tapering, the length reduction can increase to over 44%. Shortening of the device makes it possible to fabricate ring resonators with larger free spectral range, significantly increases the 1-dB bandwidth for the total transmittance, and also helps to reduce the radiative loss
Real-Time Storyboard Generation for H.264/AVC Compressed Videos

Video summarization enables convenient and efficient management of large volume of visual data. However, most existing summarization approaches are based on either the pixel domain information or conventional video compression standards. As the most recent and popular international video coding standard, H.264/AVC adopts a number of advanced techniques and brings not only opportunities but also challenges to video summarization. In this paper, we propose a real-time image storyboard generation algorithm for H.264/AVC compressed videos by using both compressed domain and pixel domain information jointly and adaptively. This algorithm extracts compressed domain information for visual content representation, video structuring and candidate representative frame selection. By fusing both compressed domain and pixel domain information, the redundancy in the candidate representative frames is further reduced. Our experimental results show that the proposed algorithm can efficiently produce image storyboards conforming to human interpretation of the essential content in generic videos.
An allocation algorithm for transporting compressed video

In this paper, we proposed a novel weight-based bandwidth allocation algorithm (WBA) for transporting compressed video traffic using feedback. Extensive simulation using a modified NIST simulator is conducted to evaluate its performance under a general weight-based share policy. Our results demonstrate that the WBA algorithm is capable of allocating bandwidth fairly for the minimum cell rate (MCR) plus weighted allocation fairness criteria among competitive compressed video sources, and is capable of reallocating smoothly when there are renegotiations of the minimum guaranteed cell rate and weight by some connections. We also show that WBA algorithm is flexible for the reallocation when a new connection is admitted. Furthermore, when unutilized network bandwidth becomes available, our algorithm is capable of exploiting it to dynamically allocate unused bandwidth.
Multi-view collaborative segmentation for prostate MRI images

Prostate delineation from MRI images is a prolonged challenging issue partially due to appearance variations across patients and disease progression. To address these challenges, our proposed collaborative method takes into account the computed multiple label-relevance maps as multiple views for learning the optimal boundary delineation. In our method, we firstly extracted multiple label-relevance maps to represent the affinities between each unlabeled pixel to the pre-defined labels to avoid the selection of handcrafted features. Then these maps were incorporated in a collaborative clustering to learn the adaptive weights for an optimal segmentation which overcomes the seeds selection sensitivity problems. The segmentation results were evaluated over 22 prostate MRI patient studies with respect to dice similarity coefficient (DSC), absolute relative volume difference (ARVD) and average symmetric surface distance (ASSD) (mm). The results and t-Test demonstrated that the proposed method improved the segmentation accuracy and robustness and the improvement was statistically significant.
An algorithm for video monitoring under a slow moving background

In this paper, a video monitoring algorithm under a slow moving background is proposed. An affine transformation model is used to describe the background image movement and two methods are given to find the affine transformation model parameters. The affine transformation is used to check the matching performance of the two frames and subsequently a subtraction operation based on block difference is performed for scene change detection. In order to finalize the detecting result, a series of image processing operations, including the adaptive threshold, morphological dilation and erosion operation, and region labeling have to be performed. Several experiments are given to show that the proposed algorithm is efficient.
A uniform framework of representation and structure reconstruction for generic form image

In the paper, we propose a novel uniform representation of generic form, within which three typical categories are defined according to the logical layout from the view point of human vision, which are explicit, semiexplicit, and implicit style form. Under the uniform framework, the different criteria are also defined and carried out on the difference kinds of forms, based on the frame line extraction using single-connected chains. Meanwhile, two subclasses of semiexplicit form, mendable and unmendable ones, are discovered and can be reconstructed by key points matching and bounding box dilation so that we can deal with a number of variations of form structure. The experimental results have shown that our proposed framework is effective and reasonable for layout analysis and structure reconstruction.