NDLI: How to Improve Medical Image Diagnosis through Association Rules: The IDEA Method

Content Provider	IEEE Xplore Digital Library
Author	Ribeiro, M.X. Traina, A. Traina, C. Rosa, N.A. Marques, P.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Sao Paulo at Sao Carlos, Sao Carlos (Ribeiro, M.X.; Traina, A.; Traina, C.)
Abstract	In this paper we present a new method, called IDEA, which employs association rules to assist in medical image diagnosis. IDEA mines association rules, relating visual features with the knowledge gotten from specialists, and employs the associations to suggest possible diagnoses for a given medical image. IDEA incorporates two new algorithms called Omega and ACE. Omega performs simultaneously feature selection and data discretization very efficiently with linear cost on the number of feature values. ACE is a new associative classifier, which has the particular ability of suggesting multiple keywords to compose the diagnosis for a given medical image. The IDEA method has an important characteristic that makes it different from other CAD methods: it suggests multiple diagnosis hypotheses for an image and ranks them based on a measure of quality. The IDEA method was implemented in a prototype (IDEA system) for radiologists evaluate it. The radiologists showed enormous interest in employing the system to aid them in their daily work. The IDEA system was applied to real datasets and the results presented high accuracy (up to 96.7%). The results testify that association rules are well-suited to support the diagnosing task.
Starting Page	266
Ending Page	271
File Size	1366123
Page Count	6
File Format	PDF
ISBN	9780769531656
ISSN	10637125
DOI	10.1109/CBMS.2008.55
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-17
Publisher Place	Finland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biomedical imaging Medical diagnostic imaging Association rules Data mining Image analysis Coronary arteriosclerosis Colon Lungs Itemsets Digital images Data Pre-processing Computer-Aided Diagnosis Image Mining Association Rules Associative Classifier
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Computer Science Applications

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Statistical Associative Classifier with Automatic Estimation of Parameters on Computer Aided Diagnosis

ADMID: An association rule discovery for mammogram image diagnosis

Computer-aided diagnosis in chest imaging: How to improve performance and avoid reinventing the wheel

Data mining problems in medicine

SuGAR: A Framework to Support Mammogram Diagnosis

Pruned Associative Classification Technique for the Medical Image Diagnosis System

An Association Rule-Based Method to Support Medical Image Diagnosis With Efficiency

Lung Segmentation in Pulmonary CT Images using Wavelet Transform

A min-max framework of cascaded classifier with multiple instance learning for computer aided diagnosis

How to Improve Medical Image Diagnosis through Association Rules: The IDEA Method

Similar Documents

A Statistical Associative Classifier with Automatic Estimation of Parameters on Computer Aided Diagnosis

ADMID: An association rule discovery for mammogram image diagnosis

Computer-aided diagnosis in chest imaging: How to improve performance and avoid reinventing the wheel

Data mining problems in medicine

SuGAR: A Framework to Support Mammogram Diagnosis

Pruned Associative Classification Technique for the Medical Image Diagnosis System

An Association Rule-Based Method to Support Medical Image Diagnosis With Efficiency

Lung Segmentation in Pulmonary CT Images using Wavelet Transform

A min-max framework of cascaded classifier with multiple instance learning for computer aided diagnosis

How to Improve Medical Image Diagnosis through Association Rules: The IDEA Method