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Content based image retrieval

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https://www.readbyqxmd.com/read/29740749/overview-on-subjective-similarity-of-images-for-content-based-medical-image-retrieval
#1
REVIEW
Chisako Muramatsu
Computer-aided diagnosis systems for assisting the classification of various diseases have the potential to improve radiologists' diagnostic accuracy and efficiency, as reported in several studies. Conventional systems generally provide the probabilities of disease types in terms of numerical values, a method that may not be efficient for radiologists who are trained by reading a large number of images. Presentation of reference images similar to those of a new case being diagnosed can supplement the probability outputs based on computerized analysis as an intuitive guide, and it can assist radiologists in their diagnosis, reporting, and treatment planning...
May 8, 2018: Radiological Physics and Technology
https://www.readbyqxmd.com/read/29733272/sub-selective-quantization-for-learning-binary-codes-in-large-scale-image-search
#2
Yeqing Li, Wei Liu, Junzhou Huang
Recently with the explosive growth of visual content on the Internet, large-scale image search has attracted intensive attention. It has been shown that mapping high-dimensional image descriptors to compact binary codes can lead to considerable efficiency gains in both storage and performing similarity computation of images. However, most existing methods still suffer from expensive training devoted to large-scale binary code learning. To address this issue, we propose a sub-selection based matrix manipulation algorithm, which can significantly reduce the computational cost of code learning...
June 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/29696548/semantic-enhanced-query-expansion-system-for-retrieving-medical-image-notes
#3
Yiqing Zhao, Nooshin J Fesharaki, Xiaohui Li, Timothy B Patrick, Jake Luo
Most current image retrieval methods require constructing semantic metadata for representing image content. To manually create semantic metadata for medical images is time-consuming, yet it is a crucial component for query expansion. We proposed a new method for searching medical image notes that uses semantic metadata to improve query expansion and leverages a knowledge model developed specifically for the medical image domain to create relevant metadata. We used a syntactic parser and the Unified Medical Language System to analyze the corpus and store text information as semantic metadata in a knowledge model...
April 25, 2018: Journal of Medical Systems
https://www.readbyqxmd.com/read/29694429/an-effective-content-based-image-retrieval-technique-for-image-visuals-representation-based-on-the-bag-of-visual-words-model
#4
Safia Jabeen, Zahid Mehmood, Toqeer Mahmood, Tanzila Saba, Amjad Rehman, Muhammad Tariq Mahmood
For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness...
2018: PloS One
https://www.readbyqxmd.com/read/29693590/textile-retrieval-based-on-image-content-from-cdc-and-webcam-cameras-in-indoor-environments
#5
Oscar García-Olalla, Enrique Alegre, Laura Fernández-Robles, Eduardo Fidalgo, Surajit Saikia
Textile based image retrieval for indoor environments can be used to retrieve images that contain the same textile, which may indicate that scenes are related. This makes up a useful approach for law enforcement agencies who want to find evidence based on matching between textiles. In this paper, we propose a novel pipeline that allows searching and retrieving textiles that appear in pictures of real scenes. Our approach is based on first obtaining regions containing textiles by using MSER on high pass filtered images of the RGB, HSV and Hue channels of the original photo...
April 25, 2018: Sensors
https://www.readbyqxmd.com/read/29650303/generating-region-proposals-for-histopathological-whole-slide-image-retrieval
#6
Yibing Ma, Zhiguo Jiang, Haopeng Zhang, Fengying Xie, Yushan Zheng, Huaqiang Shi, Yu Zhao, Jun Shi
BACKGROUND AND OBJECTIVE: Content-based image retrieval is an effective method for histopathological image analysis. However, given a database of huge whole slide images (WSIs), acquiring appropriate region-of-interests (ROIs) for training is significant and difficult. Moreover, histopathological images can only be annotated by pathologists, resulting in the lack of labeling information. Therefore, it is an important and challenging task to generate ROIs from WSI and retrieve image with few labels...
June 2018: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/29610107/sift-meets-cnn-a-decade-survey-of-instance-retrieval
#7
Liang Zheng, Yi Yang, Qi Tian
In the early days, content-based image retrieval (CBIR) was studied with global features. Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively studied for over a decade due to the advantage of SIFT in dealing with image transformations. Recently, image representations based on the convolutional neural network (CNN) have attracted increasing interest in the community and demonstrated impressive performance. Given this time of rapid evolution, this article provides a comprehensive survey of instance retrieval over the last decade...
May 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/29544790/a-novel-biomedical-image-indexing-and-retrieval-system-via-deep-preference-learning
#8
Shuchao Pang, Mehmet A Orgun, Zhezhou Yu
BACKGROUND AND OBJECTIVES: The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images...
May 2018: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/29480232/automated-and-effective-content-based-image-retrieval-for-digital-mammography
#9
Vibhav Prakash Singh, Subodh Srivastava, Rajeev Srivastava
Nowadays, huge number of mammograms has been generated in hospitals for the diagnosis of breast cancer. Content-based image retrieval (CBIR) can contribute more reliable diagnosis by classifying the query mammograms and retrieving similar mammograms already annotated by diagnostic descriptions and treatment results. Since labels, artifacts, and pectoral muscles present in mammograms can bias the retrieval procedures, automated detection and exclusion of these image noise patterns and/or non-breast regions is an essential pre-processing step...
2018: Journal of X-ray Science and Technology
https://www.readbyqxmd.com/read/29434313/distributions-of-phytoplankton-carbohydrate-protein-and-lipid-in-the-world-oceans-from-satellite-ocean-colour
#10
Shovonlal Roy
Energy value of phytoplankton regulates the growth of higher trophic species, affecting the tropic balance and sustainability of marine food webs. Therefore, developing our capability to estimate and monitor, on a global scale, the concentrations of macromolecules that determine phytoplankton energy value, would be invaluable. Reported here are the first estimates of carbohydrate, protein, lipid, and overall energy value of phytoplankton in the world oceans, using ocean-colour data from satellites. The estimates are based on a novel bio-optical method that utilises satellite-derived bio-optical fingerprints of living phytoplankton combined with allometric relationships between phytoplankton cells and cellular macromolecular contents...
February 12, 2018: ISME Journal
https://www.readbyqxmd.com/read/29372327/content-based-image-retrieval-by-using-color-descriptor-and-discrete-wavelet-transform
#11
Rehan Ashraf, Mudassar Ahmed, Sohail Jabbar, Shehzad Khalid, Awais Ahmad, Sadia Din, Gwangil Jeon
Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features...
January 25, 2018: Journal of Medical Systems
https://www.readbyqxmd.com/read/29310218/raman-hyperspectral-imaging-as-an-effective-and-highly-informative-tool-to-study-the-diagenetic-alteration-of-fossil-bones
#12
Gregorio Dal Sasso, Ivana Angelini, Lara Maritan, Gilberto Artioli
Retrieving the pristine chemical or isotopic composition of archaeological bones is of great interest for many studies aiming to reconstruct the past life of ancient populations (i.e. diet, mobility, palaeoenvironment, age). However, from the death of the individual onwards, bones undergo several taphonomic and diagenetic processes that cause the alteration of their microstructure and composition. A detailed study on bone diagenesis has the double purpose to assess the preservation state of archaeological bones and to understand the alteration pathways, thus providing evidence that may contribute to evaluate the reliability of the retrieved information...
March 1, 2018: Talanta
https://www.readbyqxmd.com/read/29290638/estimating-surface-soil-moisture-from-smap-observations-using-a-neural-network-technique
#13
J Kolassa, R H Reichle, Q Liu, S H Alemohammad, P Gentine, K Aida, J Asanuma, S Bircher, T Caldwell, A Colliander, M Cosh, C Holifield Collins, T J Jackson, J Martínez-Fernández, H McNairn, A Pacheco, M Thibeault, J P Walker
A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction...
January 2018: Remote Sensing of Environment
https://www.readbyqxmd.com/read/29289569/simultaneous-multiplexed-imaging-of-mrna-and-proteins-with-subcellular-resolution-in-breast-cancer-tissue-samples-by-mass-cytometry
#14
Daniel Schulz, Vito Riccardo Tomaso Zanotelli, Jana Raja Fischer, Denis Schapiro, Stefanie Engler, Xiao-Kang Lun, Hartland Warren Jackson, Bernd Bodenmiller
To build comprehensive models of cellular states and interactions in normal and diseased tissue, genetic and proteomic information must be extracted with single-cell and spatial resolution. Here, we extended imaging mass cytometry to enable multiplexed detection of mRNA and proteins in tissues. Three mRNA target species were detected by RNAscope-based metal in situ hybridization with simultaneous antibody detection of 16 proteins. Analysis of 70 breast cancer samples showed that HER2 and CK19 mRNA and protein levels are moderately correlated on the single-cell level, but that only HER2, and not CK19, has strong mRNA-to-protein correlation on the cell population level...
January 24, 2018: Cell Systems
https://www.readbyqxmd.com/read/29245057/a-new-randomized-kaczmarz-based-kernel-canonical-correlation-analysis-algorithm-with-applications-to-information-retrieval
#15
Jia Cai, Yi Tang
Canonical correlation analysis (CCA) is a powerful statistical tool for detecting the linear relationship between two sets of multivariate variables. Kernel generalization of it, namely, kernel CCA is proposed to describe nonlinear relationship between two variables. Although kernel CCA can achieve dimensionality reduction results for high-dimensional data feature selection problem, it also yields the so called over-fitting phenomenon. In this paper, we consider a new kernel CCA algorithm via randomized Kaczmarz method...
February 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29213188/a-novel-framework-for-intelligent-surveillance-system-based-on-abnormal-human-activity-detection-in-academic-environments
#16
Malek Al-Nawashi, Obaida M Al-Hazaimeh, Mohamad Saraee
Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system that can perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic real-time video-based surveillance system which can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment. To develop our system, we have divided the work into three phases: preprocessing phase, abnormal human activity detection phase, and content-based image retrieval phase...
2017: Neural Computing & Applications
https://www.readbyqxmd.com/read/29197487/no-352-technical-update-the-role-of-early-comprehensive-fetal-anatomy-ultrasound-examination
#17
Ori Nevo, Richard Brown, Phyllis Glanc, Ken Lim
OBJECTIVE: This guideline presents an evidence-based technical update and recommendations for the performance of early comprehensive fetal anatomic scanning (ECFAS) at 11 to 16 weeks' gestation. OPTIONS: Patients at high risk for fetal anomalies and in whom traditional mid-second trimester transabdominal imaging may be challenging or who may benefit from earlier identification of fetal anomalies may be suitable for early fetal anatomy scanning. OUTCOMES: This practice may result in earlier identification of fetal anomalies and provide earlier intervention options in high-risk populations and/or in populations where mid-second trimester transabdominal scanning is challenging...
December 2017: Journal of Obstetrics and Gynaecology Canada: JOGC, Journal D'obstétrique et Gynécologie du Canada: JOGC
https://www.readbyqxmd.com/read/29185058/content-based-image-retrieval-for-lung-nodule-classification-using-texture-features-and-learned-distance-metric
#18
Guohui Wei, Hui Cao, He Ma, Shouliang Qi, Wei Qian, Zhiqing Ma
Similarity measurement of lung nodules is a critical component in content-based image retrieval (CBIR), which can be useful in differentiating between benign and malignant lung nodules on computer tomography (CT). This paper proposes a new two-step CBIR scheme (TSCBIR) for computer-aided diagnosis of lung nodules. Two similarity metrics, semantic relevance and visual similarity, are introduced to measure the similarity of different nodules. The first step is to search for K most similar reference ROIs for each queried ROI with the semantic relevance metric...
November 29, 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/29147954/a-fully-automatic-end-to-end-method-for-content-based-image-retrieval-of-ct-scans-with-similar-liver-lesion-annotations
#19
A B Spanier, N Caplan, J Sosna, B Acar, L Joskowicz
PURPOSE: The goal of medical content-based image retrieval (M-CBIR) is to assist radiologists in the decision-making process by retrieving medical cases similar to a given image. One of the key interests of radiologists is lesions and their annotations, since the patient treatment depends on the lesion diagnosis. Therefore, a key feature of M-CBIR systems is the retrieval of scans with the most similar lesion annotations. To be of value, M-CBIR systems should be fully automatic to handle large case databases...
January 2018: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/29105019/texture-specific-bag-of-visual-words-model-and-spatial-cone-matching-based-method-for-the-retrieval-of-focal-liver-lesions-using-multiphase-contrast-enhanced-ct-images
#20
Yingying Xu, Lanfen Lin, Hongjie Hu, Dan Wang, Wenchao Zhu, Jian Wang, Xian-Hua Han, Yen-Wei Chen
PURPOSE: The bag of visual words (BoVW) model is a powerful tool for feature representation that can integrate various handcrafted features like intensity, texture, and spatial information. In this paper, we propose a novel BoVW-based method that incorporates texture and spatial information for the content-based image retrieval to assist radiologists in clinical diagnosis. METHODS: This paper presents a texture-specific BoVW method to represent focal liver lesions (FLLs)...
January 2018: International Journal of Computer Assisted Radiology and Surgery
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