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Content based image retrieval

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https://www.readbyqxmd.com/read/28203153/bold-independent-computational-entropy-assesses-functional-donut-like-structures-in-brain-fmri-images
#1
James F Peters, Sheela Ramanna, Arturo Tozzi, Ebubekir İnan
We introduce a novel method for the measurement of information level in fMRI (functional Magnetic Resonance Imaging) neural data sets, based on image subdivision in small polygons equipped with different entropic content. We show how this method, called maximal nucleus clustering (MNC), is a novel, fast and inexpensive image-analysis technique, independent from the standard blood-oxygen-level dependent signals. MNC facilitates the objective detection of hidden temporal patterns of entropy/information in zones of fMRI images generally not taken into account by the subjective standpoint of the observer...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28187892/interactive-radiographic-image-retrieval-system
#2
Malay Kumar Kundu, Manish Chowdhury, Sudeb Das
BACKGROUND AND OBJECTIVE: Content based medical image retrieval (CBMIR) systems enable fast diagnosis through quantitative assessment of the visual information and is an active research topic over the past few decades. Most of the state-of-the-art CBMIR systems suffer from various problems: computationally expensive due to the usage of high dimensional feature vectors and complex classifier/clustering schemes. Inability to properly handle the "semantic gap" and the high intra-class versus inter-class variability problem of the medical image database (like radiographic image database)...
February 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28114059/improving-large-scale-image-retrieval-through-robust-aggregation-of-local-descriptors
#3
Syed Sameed Husain, Miroslaw Bober
Visual search and image retrieval underpin numerous applications, however the task is still challenging predominantly due to the variability of object appearance and ever increasing size of the databases, often exceeding billions of images. Prior art methods rely on aggregation of local scale-invariant descriptors, such as SIFT, via mechanisms including Bag of Visual Words (BoW), Vector of Locally Aggregated Descriptors (VLAD) and Fisher Vectors (FV). However, their performance is still short of what is required...
September 27, 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28092544/discriminative-multi-view-interactive-image-re-ranking
#4
Jun Li, Chang Xu, Wankou Yang, Changyin Sun, Dacheng Tao
-Given unreliable visual patterns and insufficient query information, content-based image retrieval (CBIR) is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose Discriminative Multi-view INTeractive Image Re-ranking (DMINTIR), which integrates User Relevance Feedback (URF) capturing users' intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28077212/decoding-the-content-of-recollection-within-the-core-recollection-network-and-beyond
#5
Preston P Thakral, Tracy H Wang, Michael D Rugg
Recollection - retrieval of qualitative information about a past event - is associated with enhanced neural activity in a consistent set of neural regions (the 'core recollection network') seemingly regardless of the nature of the recollected content. Here, we employed multi-voxel pattern analysis (MVPA) to assess whether retrieval-related functional magnetic resonance imaging (fMRI) activity in core recollection regions - including the hippocampus, angular gyrus, medial prefrontal cortex, retrosplenial/posterior cingulate cortex, and middle temporal gyrus - contain information about studied content and thus demonstrate retrieval-related 'reinstatement' effects...
December 22, 2016: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
https://www.readbyqxmd.com/read/28069515/a-new-method-of-content-based-medical-image-retrieval-and-its-applications-to-ct-imaging-sign-retrieval
#6
Ling Ma, Xiabi Liu, Yan Gao, Yanfeng Zhao, Xinming Zhao, Chunwu Zhou
This paper proposes a new method of content based medical image retrieval through considering fused, context-sensitive similarity. Firstly, we fuse the semantic and visual similarities between the query image and each image in the database as their pairwise similarities. Then, we construct a weighted graph whose nodes represent the images and edges measure their pairwise similarities. By using the shortest path algorithm over the weighted graph, we obtain a new similarity measure, context-sensitive similarity measure, between the query image and each database image to complete the retrieval process...
January 6, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28029355/medial-temporal-lobe-reinstatement-of-content-specific-details-predicts-source-memory
#7
Jackson C Liang, Alison R Preston
Leading theories propose that when remembering past events, medial temporal lobe (MTL) structures reinstate the neural patterns that were active when those events were initially encoded. Accurate reinstatement is hypothesized to support detailed recollection of memories, including their source. While several studies have linked cortical reinstatement to successful retrieval, indexing reinstatement within the MTL network and its relationship to memory performance has proved challenging. Here, we addressed this gap in knowledge by having participants perform an incidental encoding task, during which they visualized people, places, and objects in response to adjective cues...
October 12, 2016: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
https://www.readbyqxmd.com/read/27981409/medical-image-retrieval-using-vector-quantization-and-fuzzy-s-tree
#8
Jana Nowaková, Michal Prílepok, Václav Snášel
The aim of the article is to present a novel method for fuzzy medical image retrieval (FMIR) using vector quantization (VQ) with fuzzy signatures in conjunction with fuzzy S-trees. In past times, a task of similar pictures searching was not based on searching for similar content (e.g. shapes, colour) of the pictures but on the picture name. There exist some methods for the same purpose, but there is still some space for development of more efficient methods. The proposed image retrieval system is used for finding similar images, in our case in the medical area - in mammography, in addition to the creation of the list of similar images - cases...
February 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/27933251/robust-image-hashing-using-ring-partition-pgnmf-and-local-features
#9
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
https://www.readbyqxmd.com/read/27908158/similarity-measurement-of-lung-masses-for-medical-image-retrieval-using-kernel-based-semisupervised-distance-metric
#10
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
https://www.readbyqxmd.com/read/27860165/construction-implementation-and-testing-of-an-image-identification-system-using-computer-vision-methods-for-fruit-flies-with-economic-importance-diptera-tephritidae
#11
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, which might negatively impact international fruit trade. Automatic or semi-automatic identification of fruit flies are greatly 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 six genera, which include the majority of pests in the Tephritidae...
November 17, 2016: Pest Management Science
https://www.readbyqxmd.com/read/27688597/dictionary-pruning-with-visual-word-significance-for-medical-image-retrieval
#12
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
https://www.readbyqxmd.com/read/27678255/content-based-image-retrieval-system-for-pulmonary-nodules-assisting-radiologists-in-self-learning-and-diagnosis-of-lung-cancer
#13
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...
February 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27662689/breast-histopathological-image-retrieval-based-on-latent-dirichlet-allocation
#14
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
https://www.readbyqxmd.com/read/27577481/assessment-of-content-based-image-retrieval-approaches-for-mammography-based-on-breast-density-patterns
#15
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
https://www.readbyqxmd.com/read/27561754/a-multimodal-search-engine-for-medical-imaging-studies
#16
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...
February 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27553081/selecting-relevant-3d-image-features-of-margin-sharpness-and-texture-for-lung-nodule-retrieval
#17
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
https://www.readbyqxmd.com/read/27521299/scalable-histopathological-image-analysis-via-supervised-hashing-with-multiple-features
#18
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
https://www.readbyqxmd.com/read/27460071/automatic-weighing-attribute-to-retrieve-similar-lung-cancer-nodules
#19
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
https://www.readbyqxmd.com/read/27364089/automated-otolith-image-classification-with-multiple-views-an-evaluation-on-sciaenidae
#20
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
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