Paper Info
Title
IFM3IRS: Information fusion retrieval system with knowledge-assisted text and visual features based on medical conceptual model
Abstract
The technology of medical data production has been rapidly changed over the past few years. Modern computer technology has created the possibility of creating multi-modal medical images. Medical data often contain multi-modal information such as visual information (image) as well as textual information. Both types of information are important for medical retrieval system (MRS). Due to the information limitation at different levels of sources, the application of information fusion becomes a real need in medical application. In this research, an information fusion framework was built to develop the multi-modality medical image retrieval system (IFM3IRS). The framework utilizes two sources of information involving text and visual-based retrieval process. The application is based on sequential order where the result from text-based process will automatically be the input in visual-based process. The main contributions of this paper are the development of a new ranking model called MedHieCon ranking model which applies semantic concepts of modality, anatomy and pathology in text-based process and also the learning approach of medical images using medical concept model in visual-based process. ImageCLEFmed 2010 data collection was used to evaluate IFM3IRS and it shows that our information fusion framework is in top list among other researchers. Although text-based retrieval system has proven to be a better performance in MRS; it is significant to determine the overall performance improvements which include the fusion of text and image.
Year
DOI
Venue
2015
10.1007/s11042-013-1792-2
Multimedia Tools and Applications
Keywords
Field
DocType
Information fusion,Late fusion technique,Text-based retrieval,Visual-based image retrieval,Query expansion,Boolean model,Supervised classification
Computer vision,Cognitive models of information retrieval,Human–computer information retrieval,Query expansion,Information retrieval,Computer science,Image retrieval,Relevance (information retrieval),Artificial intelligence,Vector space model,Concept search,Visual Word
Journal
Volume
Issue
ISSN
74
11
1380-7501
Citations 
PageRank 
References 
0
0.34
24
Authors
3
Name
Order
Citations
PageRank
Hizmawati Madzin111.70
Roziati Zainuddin2658.91
Nurfadhlina Mohd Sharef36011.72