nils-herrmann/clean_scirepeval huggingface dataset:
A web-based image viewer for multiple PET-CT follow-up studies

There exist many viewers for single-modal medical images that are efficient and are equipped with powerful analysis tools. However, there is a distinct lack of efficient image viewers for multi-modality images, particularly for displaying multiple follow-up studies that depict a patient's response to treatment over time. Such viewers would be required to display large amounts of image data. In this study, we present the TAGIGEN viewer — a web-based image viewer designed specifically for the visualisation of multi-modality follow-up studies. We innovate by defining a series of dynamically generated image grid layouts that display sets of related images together in order to improve the ability to compare and assimilate the myriad images. We adopted a web-based client-server image streaming technology, thus enabling interactive navigation of the images in a computationally efficient manner. Furthermore, our web-based approach is interoperable and requires no software installation. We evaluated the ability of our viewer in displaying and understanding a patient's follow-up images in a case study with combined positron emission tomography and computed tomography (PET-CT) follow-up scans. We conducted a usability survey on 10 participants to measure the usefulness of our viewer, used as an outpatient viewer e.g. viewer designed for use by the patients, in tracking a patient's disease state across four PET-CT studies. Our initial results suggest that our viewer was able to efficiently visualise the patient data over time, and that the web-based implementation was fast (loading on average within 5.6 seconds with real-time navigation) and easy to use (overall survey score higher than 4 / 5).
Structure context of local features in realistic human action recognition

Realistic human action recognition has been emerging as a challenging research topic due to the diffculties of representing different human actions in diverse realistic scenes. In the bag-of-features model, human actions are generally represented with the distribution of local features derived from the keypoints of action videos. Various local features have been proposed to characterize those key points. However, the important structural information among the key points has not been well investigated yet. In this paper, we propose to characterize such structure information with shape context. Therefore, each keypoint is characterized with both its local visual attributes and its global structural context contributed by other keypoints. The bag-of-features model is utilized for representing each human action and SVM is employed to perform human action recognition. Experimental results on the challenging YouTube dataset and HOHA-2 dataset demonstrate that our proposed approach accounting for structural information is more effective in representing realistic human actions. In addition, we also investigate the impact of choosing different local features such as SIFT, HOG, and HOF descriptors in human action representation. It is observed that dense keypoints can better exploit the advantages of our proposed approach.
Lung image patch classification with automatic feature learning

Image patch classification is an important task in many different medical imaging applications. The classification performance is usually highly dependent on the effectiveness of image feature vectors. While many feature descriptors have been proposed over the past years, they can be quite complicated and domain-specific. Automatic feature learning from image data has thus emerged as a different trend recently, to capture the intrinsic image features without manual feature design. In this paper, we propose to create multi-scale feature extractors based on an unsupervised learning algorithm; and obtain the image feature vectors by convolving the feature extractors with the image patches. The auto-generated image features are data-adaptive and highly descriptive. A simple classification scheme is then used to classify the image patches. The proposed method is generic in nature and can be applied to different imaging domains. For evaluation, we perform image patch classification to differentiate various lung tissue patterns commonly seen in interstitial lung disease (ILD), and demonstrate promising results.
An objective evaluation framework for segmentation techniques of functional positron emission tomography studies

Segmentation of multi-dimensional functional positron emission tomography (PET) studies into regions of interest (ROI) exhibiting similar temporal behavior is useful in diagnosis and evaluation of neurological images. Quantitative evaluation plays a crucial role in measuring the segmentation algorithm's performance. Due to the lack of "ground truth" available for evaluating segmentation of clinical images, automated segmentation results are usually compared with manual delineation of structures which is, however, subjective, and is difficult to perform. Alternatively, segmentation of co-registered anatomical images such as magnetic resonance imaging (MRI) can be used as the ground truth to the PET segmentation. However, this is limited to PET studies which have corresponding MRI. In this study, we introduce a framework for the objective and quantitative evaluation of functional PET study segmentation without the need for manual delineation or registration to anatomical images of the patient. The segmentation results are anatomically standardized to a functional brain atlas, where the segmentation of the corresponding MRI reference atlas image is used as the ground truth. We illustrate our evaluation framework by comparing the performance of two pixel-classification techniques based on k-means and fuzzy c-means cluster analysis, applied to clinical dynamic human brain PET studies. The experimental results show that the proposed evaluation framework is able to provide objective measures for segmentation comparison and performance.
Local Background Enclosure for RGB-D Salient Object Detection

Recent work in salient object detection has considered the incorporation of depth cues from RGB-D images. In most cases, depth contrast is used as the main feature. However, areas of high contrast in background regions cause false positives for such methods, as the background frequently contains regions that are highly variable in depth. Here, we propose a novel RGB-D saliency feature.  Enclosure (LBE) captures the spread of angular directions which are background with respect to the candidate region and the object that it is part of. We show that our feature improves over state-of-the-art RGB-D saliency approaches as well as RGB methods on the RGBD1000 and NJUDS2000 datasets.
Opacity-driven volume clipping for slice of interest (SOI) visualisation of multi-modality PET-CT volumes

Multi-modality positron emission tomography and computed tomography (PET-CT) imaging depicts biological and physiological functions (from PET) within a higher resolution anatomical reference frame (from CT). The need to efficiently assimilate the information from these co-aligned volumes simultaneously has resulted in 3D visualisation methods that depict e.g., slice of interest (SOI) from PET combined with direct volume rendering (DVR) of CT. However because DVR renders the whole volume, regions of interests (ROIs) such as tumours that are embedded within the volume may be occluded from view. Volume clipping is typically used to remove occluding structures by `cutting away' parts of the volume; this involves tedious trail-and-error tweaking of the clipping attempts until a satisfied visualisation is made, thus restricting its application. Hence, we propose a new automated opacity-driven volume clipping method for PET-CT using DVR-SOI visualisation. Our method dynamically calculates the volume clipping depth by considering the opacity information of the CT voxels in front of the PET SOI, thereby ensuring that only the relevant anatomical information from the CT is visualised while not impairing the visibility of the PET SOI. We outline the improvements of our method when compared to conventional 2D and traditional DVR-SOI visualisations.
Dense and Sparse Labeling With Multidimensional Features for Saliency Detection

Conventional low-level feature-based saliency detection methods tend to use nonrobust prior knowledge and do not perform well in complex or low-contrast images. In this paper, to address these issues in existing methods, we propose a novel deep neural network (DNN)-based dense and sparse labeling (DSL) framework for saliency detection. DSL consists of three major steps, namely, dense labeling (DL), sparse labeling (SL), and deep convolutional (DC) network. The DL and SL steps conduct initial saliency estimations with macro object contours and low-level image features, respectively, which effectively approximate the location of the salient object and generate accurate guidance channels for the DC step; the DC step, on the other hand, takes in the results of DL and SL, establishes a six-channeled input data structure (including local superpixel information), and conducts accurate final saliency classification. Our DSL framework exploits the saliency estimation guidance from both macro object contours and local low-level features, as well as utilizing the DNN for high-level saliency feature extraction. Extensive experiments are conducted on six well-recognized public data sets against 16 state-of-the-art saliency detection methods, including ten conventional feature-based methods and six learning-based methods. The results demonstrate the superior performance of DSL on various challenging cases in terms of both accuracy and robustness.
/spl lambda/ = 1.3 /spl mu/m high density InGaAs/GaAs quantum dots grown by molecular beam epitaxy

In<sub>x</sub>Ga<sub>1-x</sub>As (x=0.5-0.75) QD's of high density of 1.3 times 10<sup>11</sup> cm<sup>-2</sup> and uniform size (diameter ~ 23-nm and height ~ 4-nm) were obtained by MBE. lambda = 1.3 mum emission was observed for In<sub>0.65</sub>Ga<sub>0.35</sub>As QD's capped with In<sub>0.1</sub>Ga<sub>0.9</sub>As
Automated three-stage nucleus and cytoplasm segmentation of overlapping cells

Developing segmentation techniques for overlapping cells has become a major hurdle for automated analysis of cervical cells. In this paper, an automated three-stage segmentation approach to segment the nucleus and cytoplasm of each overlapping cell is described. First, superpixel clustering is conducted to segment the image into small coherent clusters that are used to generate a refined superpixel map. The refined superpixel map is passed to an adaptive thresholding step to initially segment the image into cellular clumps and background. Second, a linear classifier with superpixel-based features is designed to finalize the separation between nuclei and cytoplasm. Finally, edge and region based cell segmentation are performed based on edge enhancement process, gradient thresholding, morphological operations, and region properties evaluation on all detected nuclei and cytoplasm pairs. The proposed framework has been evaluated using the ISBI 2014 challenge dataset. The dataset consists of 45 synthetic cell images, yielding 270 cells in total. Compared with the state-of-the-art approaches, our approach provides more accurate nuclei boundaries, as well as successfully segments most of overlapping cells.
Lesion Detection and Characterization With Context Driven Approximation in Thoracic FDG PET-CT Images of NSCLC Studies

We present a lesion detection and characterization method for 18F-fluorodeoxyglucose positron emission tomography-computed tomography (FDG PET-CT) images of the thorax in the evaluation of patients with primary nonsmall cell lung cancer (NSCLC) with regional nodal disease. Lesion detection can be difficult due to low contrast between lesions and normal anatomical structures. Lesion characterization is also challenging due to similar spatial characteristics between the lung tumors and abnormal lymph nodes. To tackle these problems, we propose a context driven approximation (CDA) method. There are two main components of our method. First, a sparse representation technique with region-level contexts was designed for lesion detection. To discriminate low-contrast data with sparse representation, we propose a reference consistency constraint and a spatial consistent constraint. Second, a multi-atlas technique with image-level contexts was designed to represent the spatial characteristics for lesion characterization. To accommodate inter-subject variation in a multi-atlas model, we propose an appearance constraint and a similarity constraint. The CDA method is effective with a simple feature set, and does not require parametric modeling of feature space separation. The experiments on a clinical FDG PET-CT dataset show promising performance improvement over the state-of-the-art.
Realistic Human Action Recognition With Multimodal Feature Selection and Fusion

Although promising results have been achieved for human action recognition under well-controlled conditions, it is very challenging to recognize human actions in realistic scenarios due to increased difficulties such as dynamic backgrounds. In this paper, we propose to take multimodal (i.e., audiovisual) characteristics of realistic human action videos into account in human action recognition for the first time, since, in realistic scenarios, audio signals accompanying an action generally provide a cue to the nature of the action, such as phone ringing to answering the phone . In order to cope with diverse audio cues of an action in realistic scenarios, we propose to identify effective features from a large number of audio features with the generalized multiple kernel learning algorithm. The widely used space-time interest point descriptors are utilized as visual features, and a support vector machine is employed for both audio- and video-based classifications. At the final stage, fuzzy integral is utilized to fuse recognition results of both audio and visual modalities. Experimental results on the challenging Hollywood-2 Human Action data set demonstrate that the proposed approach is able to achieve better recognition performance improvement than that of integrating scene context. It is also discovered how audio context influences realistic action recognition from our comprehensive experiments.
Offset-adaptive quantization in DCT-based low-bitrate video coding

Band effect is one of the most annoying visual artifacts in digital video, and is especially obvious in low-bitrate applications, such as mobile video. This paper discusses the cause of band effect and proposes an improved quantization method, which can reduce band effect effectively in DCT-based video coding. This offset-adaptive quantization method adjusts the quantization results of every block according to adjacent blocks. The method can improve video subjective quality with ignorable additional computing cost. The method need not change standard bitstream syntax, so it can easily be applied to many DCT-based video codecs. Experimental results on practical video sequences prove that the method is valuable.
Texture analysis of tissue aging using global and cluster constrained local coding

Morphological age of various tissues provides important information about aging processes. In this paper, we propose a method for quantifying the tissue age of pharynx terminal bulb in C.elegans from microscopy images. Our method comprises two main components: (i) a multi-modal texture descriptor extracted at two scales; and (ii) an age quantification algorithm with global and cluster-constrained local coding. The performance of our proposed method is evaluated on 970 images of seven age classes in the publicly available IICBU dataset. Our method achieves 21% improvement in classification accuracy when compared to that of the benchmark platform, and our local coding algorithm outperformed the support vector machine model by 10%.
Median non-local means filtering for low SNR image denoising: Application to PET with anatomical knowledge

Denoising low signal-to-noise-ratio (SNR) images is a significant challenge since the intensity gradient due to noise elements may compete with or even exceed the intensity gradient due to features in the images. This situation can often be encountered in photon-limited medical imaging applications such as MLEM reconstructed Positron Emission Tomography (PET) images. In this study, we propose a median non-local means filter for denoising low-SNR images. The proposed method incorporates a median filtering operation indirectly in the non­local means (NLM) method, which gives more robust estimation of the weights used to average the pixels in the image. For the application of multi-modality imaging such as PET/CT, we further extended the method to incorporate anatomical side information which can be obtained from co-registered CT images without segmentation to preserve abrupt changes between organs on PET images and reduce the computational cost of weight calculations. We applied the proposed method (AMNLM) to a PET/CT simulation, a real physical phantom study and a clinical patient study with lung lesions. The results suggest that the proposed method outperforms the standard Gaussian filtering approach, anisotropic-median diffusion filtering (AMDF) and NLM in terms of visual assessment and trade-off between lesion contrast and noise.
A Unified Collaborative Multikernel Fuzzy Clustering for Multiview Data

Clustering is increasingly important for multiview data analytics and current algorithms are either based on the collaborative learning of local partitions or directly derived global clustering from multikernel learning. In this paper, we innovate a clustering model that unifies the local partitions and global clustering in a collaborative learning framework. We first construct a common multikernel space from a set of basis kernels to better reflect clustering information of each individual view. Then, considering that joint local partitions would conform to the global clustering, we fuse the local partitions and global clustering guidance as a single objective function in accordance with fuzzy clustering form. The collaborative learning strategy enables the mutual and interactive clustering from local partitions and global clustering. The validation was performed over two synthetic and four public databases and the clustering accuracy was measured by normalized mutual information and rand index. The experimental results demonstrated that the proposed algorithm outperformed the related state-of-the-art algorithms in comparison, which included multitask, multikernel, and multiview clustering approaches.
Realistic Human Action Recognition with Audio Context

Recognizing human actions in realistic scenes has emerged as a challenging topic due to various aspects such as dynamic backgrounds. In this paper, we present a novel approach to taking audio context into account for better action recognition performance, since audio can provide strong evidence to certain actions such as phone-ringing to answer-phone. At first, classifiers are established for visual and audio modalities, respectively. Specifically, bag of visual-words model is employed to represent human actions in visual modality, a number of audio features are extracted for audio modality, and Support Vector Machine (SVM) is employed as the classification technique. Then, a decision fusion scheme is utilized to fuse classification results from two modalities. Since audio context is not always helpful, two simple yet effective decision rules are developed for selective fusion. Experimental results on the Hollywood Human Actions (HOHA) dataset demonstrate that the proposed approach can achieve better recognition performance than that of integrating scene context. Therefor, our work provides strong confidence to further explore how audio context influences realistic human action recognition.
Generalized optimal quantitative index of dual-time FDG-PET imaging in lung cancer diagnosis

It has been recognized that dual-time FDG-PET imaging can facilitate the differentiation of malignancy from benign lesions for cancer diagnosis. However, dual-time imaging protocols with retention index (RI) as a criteria in the classification are usually defined empirically, which might lead to the low accuracy in differentiation. Recently, a new quantitative index (QI) has been proposed to improve diagnostic efficacy. In this paper, we propose a novel framework to derive generalized optimal QI and its associated optimal ranges of imaging protocol to further improve the performance of dual-time FDG-PET imaging in lung cancer diagnosis.
Image noise level estimation based on a new adaptive superpixel classification

Accurate estimation of noise level in images plays an important role in different image processing applications. The current algorithms can precisely estimate noise with smooth images, but it is still the challenge to approximate noise level from richly textured images. In this paper, we proposed a new adaptive superpixel classification algorithm for noise estimation in complicated textured images. Firstly, our new superpixel algorithm adapts the finite Gaussian clustering approach, which can better approximate homogeneous patches in noisy images. Then noise information is obtained locally from each superpixel patch. Finally, the best estimation of noise level is calculated with a statistical approach. Experimental results with various kinds of images demonstrate that our method is more accurate and robust compared to the five existing common used algorithms.
Corrections to "Robust Model for Segmenting Images With/Without Intensity Inhomogeneities" [August 13 3296-3309]

Equation (16) in the above paper (ibid., vol. 22, no. 8, pp. 3296-3309, Aug. 2013) contained an error in the numerator.  in the same paper contained errors in both the numerator and the denominator. The corrected versions of both equations are presented here.
Medical image classification with convolutional neural network

Image patch classification is an important task in many different medical imaging applications. In this work, we have designed a customized Convolutional Neural Networks (CNN) with shallow convolution layer to classify lung image patches with interstitial lung disease (ILD). While many feature descriptors have been proposed over the past years, they can be quite complicated and domain-specific. Our customized CNN framework can, on the other hand, automatically and efficiently learn the intrinsic image features from lung image patches that are most suitable for the classification purpose. The same architecture can be generalized to perform other medical image or texture classification tasks.
A multi-channel framework for image watermarking

In this paper, a novel multi-channel image watermarking framework which is able to resist geometric attacks is proposed, and a spatial domain watermarking scheme which benefits from this framework shows a high robustness to geometric attacks. This spatial domain watermarking scheme generates a watermarking template from image channels data, and then this watermarking template is embedded into the image. Because both the watermarking template and watermarking image undergo a similar geometric attack, self-synchronization can be obtained in the procedure of detecting watermarking. Experimental results demonstrate that this spatial watermarking produced by the multi-channel framework achieves a high performance of robustness even if an image undergoes some serious geometric distortion attacks.