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Content based image retrieval

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
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
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
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
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
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
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
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...
December 2, 2017: Neural Networks: the Official Journal of the International Neural Network Society
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
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
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
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...
November 16, 2017: International Journal of Computer Assisted Radiology and Surgery
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)...
November 5, 2017: International Journal of Computer Assisted Radiology and Surgery
Dirk Tiede, Andrea Baraldi, Martin Sudmanns, Mariana Belgiu, Stefan Lang
Spatiotemporal analytics of multi-source Earth observation (EO) big data is a pre-condition for semantic content-based image retrieval (SCBIR). As a proof of concept, an innovative EO semantic querying (EO-SQ) subsystem was designed and prototypically implemented in series with an EO image understanding (EO-IU) subsystem. The EO-IU subsystem is automatically generating ESA Level 2 products (scene classification map, up to basic land cover units) from optical satellite data. The EO-SQ subsystem comprises a graphical user interface (GUI) and an array database embedded in a client server model...
2017: European Journal of Remote Sensing
Guohui Wei, He Ma, Wei Qian, Hongyang Jiang, Xinzhuo Zhao
Similarity metric of the lung nodules can be useful in differentiating between benign and malignant lung nodule lesions on computed tomography (CT). Unlike previous computerized schemes, which focus on the features extracting, we concentrate on similarity metric of the lung nodules. In this study, we first assemble a lung nodule dataset which is from LIDC-IDRI lung CT images. This dataset includes 746 lung nodules in which 375 domain radiologists identified malignant nodules and 371 domain radiologists-identified benign nodules...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Jing Zhang, Wenhao Geng, Xi Liang, Jiafeng Li, Li Zhuo, Qianlan Zhou
Although many content-based image retrieval systems have been developed, few studies have focused on hyperspectral remote sensing images. In this paper, a hyperspectral remote sensing image retrieval system based on spectral and texture features is proposed. The main contributions are fourfold: (1) considering the "mixed pixel" in the hyperspectral image, endmembers as spectral features are extracted by an improved automatic pixel purity index algorithm, then the texture features are extracted with the gray level co-occurrence matrix; (2) similarity measurement is designed for the hyperspectral remote sensing image retrieval system, in which the similarity of spectral features is measured with the spectral information divergence and spectral angle match mixed measurement and in which the similarity of textural features is measured with Euclidean distance; (3) considering the limited ability of the human visual system, the retrieval results are returned after synthesizing true color images based on the hyperspectral image characteristics; (4) the retrieval results are optimized by adjusting the feature weights of similarity measurements according to the user's relevance feedback...
June 1, 2017: Applied Optics
Mengqiu Hu, Yang Yang, Fumin Shen, Ning Xie, Heng Tao Shen
Large-scale search methods are increasingly critical for many content-based visual analysis applications, among which hashing-based approximate nearest neighbor search techniques have attracted broad interests due to their high efficiency in storage and retrieval. However, existing hashing works are commonly designed for measuring data similarity by the Euclidean distances. In this paper, we focus on the problem of learning compact binary codes using the cosine similarity. Specifically, we proposed novel angular reconstructive embeddings (ARE) method, which aims at learning binary codes by minimizing the reconstruction error between the cosine similarities computed by original features and the resulting binary embeddings...
February 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Peizhong Liu, Jing-Ming Guo, Chi-Yi Wu, Danlin Cai
This paper presents an effective image retrieval method by combining high-level features from Convolutional Neural Network (CNN) model and low-level features from Dot-Diffused Block Truncation Coding (DDBTC). The low-level features, e.g., texture and color, are constructed by VQ-indexed histogram from DDBTC bitmap, maximum, and minimum quantizers. Conversely, high-level features from CNN can effectively capture human perception. With the fusion of the DDBTC and CNN features, the extended deep learning two-layer codebook features (DL-TLCF) is generated using the proposed two-layer codebook, dimension reduction, and similarity reweighting to improve the overall retrieval rate...
August 29, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Jamil Ahmad, Khan Muhammad, Sung Wook Baik
In recent years, image databases are growing at exponential rates, making their management, indexing, and retrieval, very challenging. Typical image retrieval systems rely on sample images as queries. However, in the absence of sample query images, hand-drawn sketches are also used. The recent adoption of touch screen input devices makes it very convenient to quickly draw shaded sketches of objects to be used for querying image databases. This paper presents a mechanism to provide access to visual information based on users' hand-drawn partially colored sketches using touch screen devices...
2017: PloS One
Hyunmin Kim, Do-Young Kim, Kyung-Il Joo, Jung-Hye Kim, Soon Moon Jeong, Eun Seong Lee, Jeong-Hoon Hahm, Kyuhyung Kim, Dae Woon Moon
In this study, we used spectrally focused coherent anti-Stokes Raman scattering (spCARS) microscopy assisted by sum-frequency generation (SFG) to monitor the variations in the structural morphology and molecular vibrations of a live muscle of Caenorhabditis elegans. The subunits of the muscle sarcomeres, such as the M-line, myosin, dense body, and α-actinin, were alternatively observed using spCARS microscopy for different sample orientations, with the guidance of a myosin positional marker captured by SFG microscopy...
August 23, 2017: Scientific Reports
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