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Content based image retrieval

Ram Kumar Karsh, R H Laskar, Bhanu Bhai Richhariya
BACKGROUND: Image authentication is one of the challenging research areas in the multimedia technology due to the availability of image editing tools. Image hash may be used for image authentication which should be invariant to perceptually similar image and sensitive to content changes. The challenging issue in image hashing is to design a system which simultaneously provides rotation robustness, desirable discrimination, sensitivity and localization of forged area with minimum hash length...
2016: SpringerPlus
Guohui Wei, He Ma, Wei Qian, Min Qiu
PURPOSE: To develop a new algorithm to measure the similarity between the query lung mass and reference lung mass data set for content-based medical image retrieval (CBMIR). METHODS: A lung mass data set including 746 mass regions of interest (ROIs) was assembled. Among them, 375 ROIs depicted malignant lesions and 371 depicted benign lesions. Each mass ROI is represented by a vector of 26 texture features. A kernel function was employed to map the original data in input space to a feature space...
December 2016: Medical Physics
Jiang-Ning Wang, Xiao-Lin Chen, Xin-Wen Hou, Li-Bing Zhou, Chao-Dong Zhu, Li-Qiang Ji
BACKGROUND: Many species of Tephritidae are damaging to fruit that might negatively impact international fruit trade. Automatic or semi-automatic identification of fruit flies are highly needed for diagnosing causes of damage and quarantine protocols for economically relevant insects. RESULTS: A fruit fly image identification system named AFIS1.0 has been developed using 74 species belonging to 6 genera, which include majority pests in Tephritidae. The system combines automated image identification and manual verification, balancing operability and accuracy...
November 17, 2016: Pest Management Science
Fan Zhang, Yang Song, Weidong Cai, Alexander G Hauptmann, Sidong Liu, Sonia Pujol, Ron Kikinis, Michael J Fulham, David Dagan Feng, Mei Chen
Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic...
February 12, 2016: Neurocomputing
Ashis Kumar Dhara, Sudipta Mukhopadhyay, Anirvan Dutta, Mandeep Garg, Niranjan Khandelwal
Visual information of similar nodules could assist the budding radiologists in self-learning. This paper presents a content-based image retrieval (CBIR) system for pulmonary nodules, observed in lung CT images. The reported CBIR systems of pulmonary nodules cannot be put into practice as radiologists need to draw the boundary of nodules during query formation and feature database creation. In the proposed retrieval system, the pulmonary nodules are segmented using a semi-automated technique, which requires a seed point on the nodule from the end-user...
September 27, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Yibing Ma, Zhiguo Jiang, Haopeng Zhang, Fengying Xie, Yushan Zheng, Huaqiang Shi, Yu Zhao
In the field of pathology, whole slide image (WSI) has become the major carrier of visual and diagnostic information. Content based image retrieval among WSIs can aid the diagnosis of an unknown pathological image by finding its similar regions in WSIs with diagnostic information. However, the huge size and complex content of WSI pose several challenges for retrieval. In this paper, we propose an unsupervised, accurate and fast retrieval method for breast histopathological image. Specifically, the method presents local statistical feature of nuclei for morphology and distribution of nuclei, and employs Gabor feature to describe texture information...
September 20, 2016: IEEE Journal of Biomedical and Health Informatics
Nabila Sabatini Purwadi, Hüseyin Tanzer Atay, Kenan Kaan Kurt, Serkan Turkeli
This study is trying to assess methods commonly used in content-based image retrieval (CBIR) for screening mammography analysis. A database consists of 12 different BI-RADS classes related to breast density patterns of mammogram patches which are taken from IRMA database is used in this study. Three feature extraction methods, namely grey-level co-occurrence matrix (GLCM), principal component analysis (PCA), and scale-invariant feature transform (SIFT) are being investigated and compared with prior studies...
2016: Studies in Health Technology and Informatics
Eduardo Pinho, Tiago Godinho, Frederico Valente, Carlos Costa
The use of digital medical imaging systems in healthcare institutions has increased significantly, and the large amounts of data in these systems have led to the conception of powerful support tools: recent studies on content-based image retrieval (CBIR) and multimodal information retrieval in the field hold great potential in decision support, as well as for addressing multiple challenges in healthcare systems, such as computer-aided diagnosis (CAD). However, the subject is still under heavy research, and very few solutions have become part of Picture Archiving and Communication Systems (PACS) in hospitals and clinics...
August 25, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
José Raniery Ferreira, Paulo Mazzoncini de Azevedo-Marques, Marcelo Costa Oliveira
PURPOSE: Lung cancer is the leading cause of cancer-related deaths in the world. Its diagnosis is a challenge task to specialists due to several aspects on the classification of lung nodules. Therefore, it is important to integrate content-based image retrieval methods on the lung nodule classification process, since they are capable of retrieving similar cases from databases that were previously diagnosed. However, this mechanism depends on extracting relevant image features in order to obtain high efficiency...
August 23, 2016: International Journal of Computer Assisted Radiology and Surgery
Menglin Jiang, Shaoting Zhang, Junzhou Huang, Lin Yang, Dimitris N Metaxas
Histopathology is crucial to diagnosis of cancer, yet its interpretation is tedious and challenging. To facilitate this procedure, content-based image retrieval methods have been developed as case-based reasoning tools. Especially, with the rapid growth of digital histopathology, hashing-based retrieval approaches are gaining popularity due to their exceptional efficiency and scalability. Nevertheless, few hashing-based histopathological image analysis methods perform feature fusion, despite the fact that it is a common practice to improve image retrieval performance...
December 2016: Medical Image Analysis
David Jones Ferreira de Lucena, José Raniery Ferreira Junior, Aydano Pamponet Machado, Marcelo Costa Oliveira
BACKGROUND: Cancer is a disease characterized as an uncontrolled growth of abnormal cells that invades neighboring tissues and destroys them. Lung cancer is the primary cause of cancer-related deaths in the world, and it diagnosis is a complex task for specialists and it presents some big challenges as medical image interpretation process, pulmonary nodule detection and classification. In order to aid specialists in the early diagnosis of lung cancer, computer assistance must be integrated in the imaging interpretation and pulmonary nodule classification processes...
2016: BMC Medical Informatics and Decision Making
J Y Wong, C Chu, V C Chong, S K Dhillon, K H Loh
Combined multiple 2D views (proximal, anterior and ventral aspects) of the sagittal otolith are proposed here as a method to capture shape information for fish classification. Classification performance of single view compared with combined 2D views show improved classification accuracy of the latter, for nine species of Sciaenidae. The effects of shape description methods (shape indices, Procrustes analysis and elliptical Fourier analysis) on classification performance were evaluated. Procrustes analysis and elliptical Fourier analysis perform better than shape indices when single view is considered, but all perform equally well with combined views...
August 2016: Journal of Fish Biology
Nouman Ali, Khalid Bashir Bajwa, Robert Sablatnig, Savvas A Chatzichristofis, Zeshan Iqbal, Muhammad Rashid, Hafiz Adnan Habib
With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR), high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). The two local features representations are selected for image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination...
2016: PloS One
Osman H Ahmed, Hopin Lee, Laura L Struik
BACKGROUND AND AIM: Recently image-sharing social media platforms have become a popular medium for sharing health-related images and associated information. However within the field of sports medicine, and more specifically sports related concussion, the content of images and meta-data shared through these popular platforms have not been investigated. The aim of this study was to analyse the content of concussion-related images and its accompanying meta-data on image-sharing social media platforms...
September 2016: Physical Therapy in Sport
Jeffrey R Binder, Lisa L Conant, Colin J Humphries, Leonardo Fernandino, Stephen B Simons, Mario Aguilar, Rutvik H Desai
Componential theories of lexical semantics assume that concepts can be represented by sets of features or attributes that are in some sense primitive or basic components of meaning. The binary features used in classical category and prototype theories are problematic in that these features are themselves complex concepts, leaving open the question of what constitutes a primitive feature. The present availability of brain imaging tools has enhanced interest in how concepts are represented in brains, and accumulating evidence supports the claim that these representations are at least partly "embodied" in the perception, action, and other modal neural systems through which concepts are experienced...
May 2016: Cognitive Neuropsychology
Shiv Ram Dubey, Satish Kumar Singh, Rajat Kumar Singh
Local binary pattern (LBP) is widely adopted for efficient image feature description and simplicity. To describe the color images, it is required to combine the LBPs from each channel of the image. The traditional way of binary combination is to simply concatenate the LBPs from each channel, but it increases the dimensionality of the pattern. In order to cope with this problem, this paper proposes a novel method for image description with multichannel decoded LBPs. We introduce adder- and decoder-based two schemas for the combination of the LBPs from more than one channel...
September 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Jun Cheng, Wei Yang, Meiyan Huang, Wei Huang, Jun Jiang, Yujia Zhou, Ru Yang, Jie Zhao, Yanqiu Feng, Qianjin Feng, Wufan Chen
Content-based image retrieval (CBIR) techniques have currently gained increasing popularity in the medical field because they can use numerous and valuable archived images to support clinical decisions. In this paper, we concentrate on developing a CBIR system for retrieving brain tumors in T1-weighted contrast-enhanced MRI images. Specifically, when the user roughly outlines the tumor region of a query image, brain tumor images in the database of the same pathological type are expected to be returned. We propose a novel feature extraction framework to improve the retrieval performance...
2016: PloS One
Rachel Sparks, Anant Madabhushi
Content-based image retrieval (CBIR) retrieves database images most similar to the query image by (1) extracting quantitative image descriptors and (2) calculating similarity between database and query image descriptors. Recently, manifold learning (ML) has been used to perform CBIR in a low dimensional representation of the high dimensional image descriptor space to avoid the curse of dimensionality. ML schemes are computationally expensive, requiring an eigenvalue decomposition (EVD) for every new query image to learn its low dimensional representation...
2016: Scientific Reports
D Abraham Chandy, A Hepzibah Christinal, Alwyn John Theodore, S Easter Selvan
Content-based image retrieval plays an increasing role in the clinical process for supporting diagnosis. This paper proposes a neighbourhood search method to select the near-optimal feature subsets for the retrieval of mammograms from the Mammographic Image Analysis Society (MIAS) database. The features based on grey level cooccurrence matrix, Daubechies-4 wavelet, Gabor, Cohen-Daubechies-Feauveau 9/7 wavelet and Zernike moments are extracted from mammograms available in the MIAS database to form the combined or fused feature set for testing various feature selection methods...
June 4, 2016: Medical & Biological Engineering & Computing
Margriet van Iersel, Corine H M Latour, Rien de Vos, Paul A Kirschner, Wilma J M Scholte Op Reimer
OBJECTIVES: To review recent literature on student nurses' perceptions of different areas of nursing practice, in particular community care. Healthcare is changing from care delivery in institutional settings to care to patients in their own homes. Problematic is that nursing students do not see community care as an attractive line of work, and their perceptions of community care do not reflect the realities of the profession. Understanding the factors influencing the perception of the professional field is important to positively influence students' willingness to see community nursing as a future profession...
September 2016: International Journal of Nursing Studies
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