Content-Based Image Retrieval for Medical Image Analysis

2012 ◽  
pp. 202-219
Author(s):  
Jianhua Yao ◽  
Ronald M. Summers
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Annotation ◽  
Database Systems ◽  
Research Field ◽  
Content Based Image Retrieval ◽  
Image Management ◽  
Interaction Database ◽  
Active Research ◽  
Main Components

The growing repositories of clinical imaging data generate a need for effective image management and access that demands more than simple text-based queries. Content-Based Image Retrieval (CBIR) is an active research field and has drawn attention in recent years. It is a technique to organize and search image archives by their visual content. It is a multi-discipline field that integrates technologies from computer vision, machine learning, information retrieval, human-machine interaction, database systems, and data mining. CBIR consists of four main components: database and indexing, feature extraction, query formation and interface, and similarity measures. The applications of CBIR to the medical field include PACS integration, image annotation/codification, computer-aided diagnosis, case-based reasoning, and teaching tools. This chapter intends to disseminate the CBIR techniques to their applications to medical image management and analysis and to attract greater interest from various research communities to advance research in this field.

Content-Based Image Retrieval for Medical Image Analysis

Data Mining ◽  
2013 ◽  
pp. 1097-1113
Author(s):  
Jianhua Yao ◽  
Ronald M. Summers
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Annotation ◽  
Database Systems ◽  
Research Field ◽  
Content Based Image Retrieval ◽  
Image Management ◽  
Interaction Database ◽  
Active Research ◽  
Main Components

The growing repositories of clinical imaging data generate a need for effective image management and access that demands more than simple text-based queries. Content-Based Image Retrieval (CBIR) is an active research field and has drawn attention in recent years. It is a technique to organize and search image archives by their visual content. It is a multi-discipline field that integrates technologies from computer vision, machine learning, information retrieval, human-machine interaction, database systems, and data mining. CBIR consists of four main components: database and indexing, feature extraction, query formation and interface, and similarity measures. The applications of CBIR to the medical field include PACS integration, image annotation/codification, computer-aided diagnosis, case-based reasoning, and teaching tools. This chapter intends to disseminate the CBIR techniques to their applications to medical image management and analysis and to attract greater interest from various research communities to advance research in this field.

scholarly journals Study of Content Based Image Retrieval Using Data Mining Techniques

2020 ◽  
pp. 397-403
Author(s):  
S. Dhinakaran
Keyword(s):  
Image Retrieval ◽  
Research Area ◽  
Research Field ◽  
Content Based Image Retrieval ◽  
High Dimensional ◽  
Query Image ◽  
Clustering Techniques ◽  
Clustering Technique ◽  
Active Research ◽  
And Performance

<p>The field of image retrieval has been an active research area for several decades and has been paid more and more attention in recent years as a result of the dramatic and fast increase in the volume of digital images. Content-based image retrieval (CBIR) is a new but widely adopted method for finding images from vast and un annotated image databases. In recent years, a variety of techniques have been developed to improve the performance of CBIR. In reaction to the needs of users, who feel problems connected with traditional methods of image searching and indexing, researchers focus their interest on techniques for retrieving images on the basis of automatically-derived features, often denoted as Content-Based Image Retrieval (CBIR). CBIR systems index the media documents using salient features extracted from the actual media rather than by textual annotations. Query by content is nowadays a very active research field, with many systems being developed by industrial and academic teams. Results performed by these teams are really promising. The situation gets diametrically different when we move our attention from the usual CBIR task, i.e. the retrieval of images which are similar (as a whole) to the query image, to the task “find all images that contain the query image”. The proposed CBIR technique uses more than one clustering techniques to improve the performance of CBIR. This optimized method makes use of K-means and Hierarchical clustering technique to improve the execution time and performance of image retrieval systems in high dimensional sets. In this similarity measure is totally based on colors. In this paper more focus area is the way of combination of clustering technique in order to get faster output of images. In this paper the clustering techniques are discussed and analyzed. Also, we propose a method HDK that uses more than one clustering technique to improve the performance of CBIR. This method makes use of hierarchical and divides and conquers K-means clustering technique with equivalency and compatible relation concepts to improve the performance of the K-Means for using in high dimensional datasets. It also introduced the feature like color, texture and shape for accurate and effective retrieval system.</p>

Annotation of Medical Images

2013 ◽  
pp. 74-90 ◽  
Author(s):  
Chia-Hung Wei ◽  
Sherry Y. Chen
Keyword(s):  
Medical Image ◽  
Image Annotation ◽  
Medical Images ◽  
Research Field ◽  
Automatic Annotation ◽  
Image Management ◽  
Automatic Image Annotation ◽  
Linguistic Terms ◽  
Advance Research

Automatic image annotation is a technique that automatically assigns a set of linguistic terms to images in order to categorize the images conceptually and provide means for effectively accessing images from databases. This chapter firstly introduces fundamentals and techniques of automatic image annotation to give an overview of this research field. A case study, which describes the methodology for annotating mammographic lesions, is then presented. This chapter is intended to disseminate the knowledge of the automatic annotation approaches to the applications of medical image management and to attract greater interest from various research communities to rapidly advance research in this field.

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

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.

Content Based Image Retrieval Using Active-Nets

2011 ◽  
pp. 85-114 ◽  
Author(s):  
David García Pérez ◽  
Antonio Mosquera ◽  
Stefano Berretti ◽  
Alberto Del Bimbo
Keyword(s):  
Image Retrieval ◽  
Structural Information ◽  
Similarity Measures ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Image Objects ◽  
Image Retrieval System ◽  
Deformable Structure ◽  
Retrieval Problem ◽  
Active Research

Content-based image retrieval has been an active research area in past years. Many different solutions have been proposed to improve performance of retrieval, but the large part of these works have focused on sub-parts of the retrieval problem, providing targeted solutions only for individual aspects (i.e., feature extraction, similarity measures, indexing, etc). In this chapter, we first shortly review some of the main practiced solutions for content-based image retrieval evidencing some of the main issues. Then, we propose an original approach for the extraction of relevant image objects and their matching for retrieval applications, and present a complete image retrieval system which uses this approach (including similarity measures and image indexing). In particular, image objects are represented by a two-dimensional deformable structure, referred to as “active net.” Active net is capable of adapting to relevant image regions according to chromatic and edge information. Extension of the active nets has been defined, which permits the nets to break themselves, thus increasing their capability to adapt to objects with complex topological structure. The resulting representation allows a joint description of color, shape, and structural information of extracted objects. A similarity measure between active nets has also been defined and used to combine the retrieval with an efficient indexing structure. The proposed system has been experimented on two large and publicly available objects databases, namely, the ETH-80 and the ALOI.

scholarly journals Combined global and local semantic feature–based image retrieval analysis with interactive feedback

2019 ◽  
Vol 53 (1-2) ◽  
pp. 3-17
Author(s):  
A Anandh ◽  
K Mala ◽  
R Suresh Babu
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Research Work ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Image Retrieval System ◽  
False Image ◽  
Active Research ◽  
High Level ◽  
Interactive Feedback

Nowadays, user expects image retrieval systems using a large database as an active research area for the investigators. Generally, content-based image retrieval system retrieves the images based on the low-level features, high-level features, or the combination of both. Content-based image retrieval results can be improved by considering various features like directionality, contrast, coarseness, busyness, local binary pattern, and local tetra pattern with modified binary wavelet transform. In this research work, appropriate features are identified, applied and results are validated against existing systems. Modified binary wavelet transform is a modified form of binary wavelet transform and this methodology produced more similar retrieval images. The proposed system also combines the interactive feedback to retrieve the user expected results by addressing the issues of semantic gap. The quantitative evaluations such as average retrieval rate, false image acceptation ratio, and false image rejection ratio are evaluated to ensure the user expected results of the system. In addition to that, precision and recall are evaluated from the proposed system against the existing system results. When compared with the existing content-based image retrieval methods, the proposed approach provides better retrieval accuracy.

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.

Sign in / Sign up

Export Citation Format

Share Document