An Interactive Image Retrieval Framework for Biomedical Articles Based on Visual Region-of- Interest (ROI) Identification and Classification

2012 ◽  
Author(s):  
Mahmudur Rahman ◽  
Daekeun You ◽  
Matthew S. Simpson ◽  
Sameer K. Antani ◽  
Dina Demner-Fushman ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Region Of Interest ◽  
Visual Region

Semantic Localization-driven Partial Image Retrieval in CT Series

2012 ◽  
Vol 51 (06) ◽  
pp. 557-565 ◽  
Author(s):  
A. Cavallaro ◽  
H.-P. Kriegel ◽  
M. Schubert ◽  
M. Petri
Keyword(s):  
Image Retrieval ◽  
Cost Reduction ◽  
Communication Systems ◽  
Region Of Interest ◽  
Input Output ◽  
Regression Techniques ◽  
Complete Series ◽  
Ct Data ◽  
Picture Archiving And Communication ◽  
Semantic Localization

SummaryBackground: Picture archiving and communication systems (PACS) contain very large amounts of computed tomography (CT) data. When querying a PACS for a particular series, the user is often not interested in the complete series but in a certain region of interest (ROI), described e.g. by an example view in another series or an anatomical concept.Objectives: Restricting a retrieval query to such an ROI saves both loading time and navigational effort. In this paper, we propose an efficient method for defining and retrieving ROIs.Methods: We employ interpolation and regression techniques for mapping the slices of a series to a newly generated standardized height atlas of the human body.Results: Examinations of the accuracy and the saved input/output (I/O) costs of our new method on a repository of 1,360 CT series demonstrate the advantages of our system. Depending on the scope of the retrieval query, we can economize up to 99% of the total loading time.Conclusion: Our proposed method for flexible, context-based, partial image retrieval enables the user to directly focus on the relevant portion of the image material and it targets the high potential of I/O cost reduction of a common PACS.

Content-Based Medical Image Retrieval Using Delaunay Triangulation Segmentation Technique

2021 ◽  
Vol 14 (2) ◽  
pp. 48-66
Author(s):  
Sneha Kugunavar ◽  
Prabhakar C. J.
Keyword(s):  
Image Retrieval ◽  
Delaunay Triangulation ◽  
Medical Image ◽  
Region Of Interest ◽  
Texture Features ◽  
Ct Images ◽  
Medical Image Retrieval ◽  
Segmentation Technique ◽  
Geometric Moments ◽  
Benchmark Datasets

This article presents a novel technique for retrieval of lung images from the collection of medical CT images. The proposed content-based medical image retrieval (CBMIR) technique uses an automated image segmentation technique called Delaunay triangulation (DT) in order to segment lung organ (region of interest) from the original medical image. The proposed method extracts novel and discriminant features from the segmented lung region instead of extracting novel features from the whole original image. For the extraction of shape features, the authors employ edge histogram descriptor (EHD) and geometric moments (GM), and for the extraction of texture features, the authors use gray-level co-occurrence matrix (GLCM) technique. The shape and texture features are combined to form the hybrid feature which is used for retrieval of similar lung images. The proposed method is evaluated using two benchmark datasets of lung CT images. The simulation results prove that the proposed CBMIR framework shows improved performance in terms of retrieval accuracy and retrieval time.

Selective visual region of interest to enhance medical video conferencing

1998 ◽  
Author(s):  
Walt Bonneau, Jr. ◽  
Christopher J. Read ◽  
Girish Shirali
Keyword(s):  
Video Conferencing ◽  
Region Of Interest ◽  
Visual Region

Creating the Bag-of-Words with Spatial Context Information for Image Retrieval

2014 ◽  
Vol 556-562 ◽  
pp. 4788-4791
Author(s):  
Zhen Wei Li ◽  
Jing Zhang ◽  
Xin Liu ◽  
Li Zhuo
Keyword(s):  
Image Retrieval ◽  
Region Of Interest ◽  
Focus Of Attention ◽  
Spatial Context ◽  
Image Feature ◽  
Context Information ◽  
Bag Of Words ◽  
Attention Shift ◽  
Spatial Coding ◽  
Attention Model

Recently bag-of-words (BoW) model as image feature has been widely used in content-based image retrieval. Most of existing approaches of creating BoW ignore the spatial context information. In order to better describe the image content, the BoW with spatial context information is created in this paper. Firstly, image’s regions of interest are detected and the focus of attention shift is produced through visual attention model. The color and SIFT features are extracted from the region of interest and BoW is created through cluster analysis method. Secondly, the spatial context information among objects in an image is generated by using the spatial coding method based on the focus of attention shift. Then the image is represented as the model of BoW with spatial context. Finally, the model of spatial context BoW is applied into image retrieval to evaluate the performance of the proposed method. Experimental results show the proposed method can effectively improve the accuracy of the image retrieval.

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