Share Email Print

Proceedings Paper

Modeling uncertainty in classification design of a computer-aided detection system
Author(s): Rahil Hosseini; Jamshid Dehmeshki; Sarah Barman; Mahdi Mazinani; Salah Qanadli
Format Member Price Non-Member Price
PDF $17.00 $21.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

A computerized image analysis technology suffers from imperfection, imprecision and vagueness of the input data and its propagation in all individual components of the technology including image enhancement, segmentation and pattern recognition. Furthermore, a Computerized Medical Image Analysis System (CMIAS) such as computer aided detection (CAD) technology deals with another source of uncertainty that is inherent in image-based practice of medicine. While there are several technology-oriented studies reported in developing CAD applications, no attempt has been made to address, model and integrate these types of uncertainty in the design of the system components, even though uncertainty issues directly affect the performance and its accuracy. In this paper, the main uncertainty paradigms associated with CAD technologies are addressed. The influence of the vagueness and imprecision in the classification of the CAD, as a second reader, on the validity of ROC analysis results is defined. In order to tackle the problem of uncertainty in the classification design of the CAD, two fuzzy methods are applied and evaluated for a lung nodule CAD application. Type-1 fuzzy logic system (T1FLS) and an extension of it, interval type-2 fuzzy logic system (IT2FLS) are employed as methods with high potential for managing uncertainty issues. The novelty of the proposed classification methods is to address and handle all sources of uncertainty associated with a CAD system. The results reveal that IT2FLS is superior to T1FLS for tackling all sources of uncertainty and significantly, the problem of inter and intra operator observer variability.

Paper Details

Date Published: 9 March 2010
PDF: 8 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76242V (9 March 2010); doi: 10.1117/12.844178
Show Author Affiliations
Rahil Hosseini, Kingston Univ. (United Kingdom)
Jamshid Dehmeshki, Kingston Univ. (United Kingdom)
Sarah Barman, Kingston Univ. (United Kingdom)
Mahdi Mazinani, Kingston Univ. (United Kingdom)
Salah Qanadli, Univ. de Lausanne Hospital (Switzerland)

Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

© SPIE. Terms of Use
Back to Top