Combining text retrieval and content-based image retrieval for searching a large-scale medical image database in an integrated RIS/PACS environment

2009 ◽  
Author(s):  
Zhenyu He ◽  
Yanjie Zhu ◽  
Tonghui Ling ◽  
Jianguo Zhang
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Large Scale ◽  
Image Database ◽  
Text Retrieval ◽  
Content Based Image Retrieval

scholarly journals Partitioning Medical Image Databases for Content-based Queries on a Grid

2005 ◽  
Vol 44 (02) ◽  
pp. 154-160 ◽  
Author(s):  
V. Breton ◽  
I. E. Magnin ◽  
J. Montagnat
Keyword(s):  
Theoretical Model ◽  
Image Retrieval ◽  
Medical Image ◽  
Computation Time ◽  
Image Database ◽  
Image Databases ◽  
Content Based Image Retrieval ◽  
Medical Databases ◽  
Grid Nodes ◽  
The Impact

Summary Objectives: In this paper we study the impact of executing a medical image database query application on the grid. For lowering the total computation time, the image database is partitioned into subsets to be processed on different grid nodes. Methods: A theoretical model of the application complexity and estimates of the grid execution overhead are used to efficiently partition the database. Results: We show results demonstrating that smart partitioning of the database can lead to significant improvements in terms of total computation time. Conclusions: Grids are promising for content-based image retrieval in medical databases.

scholarly journals Integration of Statistical based Texture and Color Feature for Medical Image Retrieval

2019 ◽  
Vol 8 (3) ◽  
pp. 5584-5588 ◽  
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Image Retrieval ◽  
Medical Image ◽  
Spatial Information ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Retrieval Method ◽  
Medical Image Retrieval ◽  
The Common

Today, the common problem in the domain of computer vision and pattern recognition is content based image retrieval (CBIR). In this paper, a novel image retrieval method using the geometric details based on the correlation among edgels and correlation between pixels has been introduced. The autocorrelation based choridiogram descriptor has been extracted from the image to obtain geometric, texture and spatial information. Color autocorrelogram has been computed to obtain color, texture and spatial information. The proposed method is tested on benchmark heterogeneous medical image database and LIDC-IDRI-CT and VIA/I-ELCAP-CT databases and results are compared with typical CBIR system for medical image retrieval

scholarly journals Image Retrieval Based on WBCH and Clustering Algorithm

2013 ◽  
Vol 5 (3) ◽  
pp. 604-613
Author(s):  
Asmita Bhaskar Shirsath ◽  
M. J. Chouhan ◽  
N. J Uke
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Similarity Measures ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Shape Information ◽  
Image Descriptors ◽  
Image Retrieval System ◽  
Retrieval Result

Research on content-based image retrieval has gained tremendous momentum during the last decade. Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. In order to get faster  retrieval result from large-scale image database ,we proposed image retrieval system in which image database is first pre-processed by Wavelet Based Color Histogram (WBCH) and K-means algorithm and then using Hierarchical clustering algorithm we index the previous result and then by using similarity measures we retrieve the images from pre-processed database. Experiments show that this proposed method offers substantial increase in retrieval speed but needs to be improved on retrieval results.

Content Based Image Retrieval for Large Medical Image Corpus

2015 ◽  
pp. 714-725 ◽  
Author(s):  
Gjorgji Strezoski ◽  
Dario Stojanovski ◽  
Ivica Dimitrovski ◽  
Gjorgji Madjarov
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Content Based Image Retrieval

scholarly journals Fully Automated Pose Estimation of Historical Images in the Context of 4D Geographic Information Systems Utilizing Machine Learning Methods

2021 ◽  
Vol 10 (11) ◽  
pp. 748
Author(s):  
Ferdinand Maiwald ◽  
Christoph Lehmann ◽  
Taras Lazariv
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Image Retrieval ◽  
Pose Estimation ◽  
Large Scale ◽  
Feature Matching ◽  
Geographic Information ◽  
Content Based Image Retrieval ◽  
Matching Methods ◽  
Historical Images

The idea of virtual time machines in digital environments like hand-held virtual reality or four-dimensional (4D) geographic information systems requires an accurate positioning and orientation of urban historical images. The browsing of large repositories to retrieve historical images and their subsequent precise pose estimation is still a manual and time-consuming process in the field of Cultural Heritage. This contribution presents an end-to-end pipeline from finding relevant images with utilization of content-based image retrieval to photogrammetric pose estimation of large historical terrestrial image datasets. Image retrieval as well as pose estimation are challenging tasks and are subjects of current research. Thereby, research has a strong focus on contemporary images but the methods are not considered for a use on historical image material. The first part of the pipeline comprises the precise selection of many relevant historical images based on a few example images (so called query images) by using content-based image retrieval. Therefore, two different retrieval approaches based on convolutional neural networks (CNN) are tested, evaluated, and compared with conventional metadata search in repositories. Results show that image retrieval approaches outperform the metadata search and are a valuable strategy for finding images of interest. The second part of the pipeline uses techniques of photogrammetry to derive the camera position and orientation of the historical images identified by the image retrieval. Multiple feature matching methods are used on four different datasets, the scene is reconstructed in the Structure-from-Motion software COLMAP, and all experiments are evaluated on a newly generated historical benchmark dataset. A large number of oriented images, as well as low error measures for most of the datasets, show that the workflow can be successfully applied. Finally, the combination of a CNN-based image retrieval and the feature matching methods SuperGlue and DISK show very promising results to realize a fully automated workflow. Such an automated workflow of selection and pose estimation of historical terrestrial images enables the creation of large-scale 4D models.

A Content-Based Approach to Medical Image Database Retrieval

2011 ◽  
pp. 281-302
Author(s):  
Chia-Hung Wei ◽  
Chang-Tsun Li ◽  
Roland Wilson
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Database Retrieval ◽  
Retrieval Systems ◽  
Large Databases ◽  
Advance Research ◽  
Intervention Content

Content-based image retrieval (CBIR) makes use of image features, such as color and texture, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. This chapter introduces a content-based approach to medical image retrieval. Fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. A case study, which describes the methodology of a CBIR system for retrieving digital mammogram database, is then presented. This chapter is intended to disseminate the knowledge of the CBIR approach to the applications of medical image management and to attract greater interest from various research communities to rapidly advance research in this field.

Medical Image Retrieval Based on Semi-Supervised Learning

2010 ◽  
Vol 108-111 ◽  
pp. 201-206 ◽  
Author(s):  
Hui Liu ◽  
Cai Ming Zhang ◽  
Hua Han
Keyword(s):  
Support Vector Machine ◽  
Active Learning ◽  
Image Retrieval ◽  
Supervised Learning ◽  
Relevance Feedback ◽  
Medical Image ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Learning Support ◽  
Medical Image Retrieval

Among various content-based image retrieval (CBIR) methods based on active learning, support vector machine(SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. Furthermore, it’s difficult to collect vast amounts of labeled data and easy for unlabeled data to image examples. Therefore, it is necessary to define conditions to utilize the unlabeled examples enough. This paper presented a method of medical images retrieval about semi-supervised learning based on SVM for relevance feedback in CBIR. This paper also introduced an algorithm about defining two learners, both learners are re-trained after every relevance feedback round, and then each of them gives every image in a rank. Experiments show that using semi-supervised learning idea in CBIR is beneficial, and the proposed method achieves better performance than some existing methods.

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