Fuzzy propositions weighted by veracities or how to relate fuzzy logic and probability theory for segmentation of ultrasound images

Renaud Debon; Basel Solaiman; Jean-Michel Cauvin; Michel Robaszkiewicz; Christian Roux

doi:10.1117/12.460244

12 March 2002 Fuzzy propositions weighted by veracities or how to relate fuzzy logic and probability theory for segmentation of ultrasound images

Renaud Debon, Basel Solaiman, Jean-Michel Cauvin, Michel Robaszkiewicz, Christian Roux

Author Affiliations +

Proceedings Volume 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV; (2002) https://doi.org/10.1117/12.460244
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

In medical imaging, and more generally in medical information, researches go towards fusion systems. Nowadays, the steps of information source definition, the pertinent data extraction and the fusion need to be conducted as a whole. In this work, our interest is related to the esophagus wall segmentation from ultrasound images sequences. We aim to elaborate a general methodology of data mining that coherently links works on data selection and fusion architectures, in order to extract useful information from raw data and to integrate efficiently the physician a prior. In the presented method, based on fuzzy logic, some fuzzy propositions are defined using physicians a prior knowledge. The use of probabilistic distributions, estimated thanks to a learning base of pathologic and non-pathologic cases, enables the veracity of these propositions to be qualified. This promising idea enables information to be managed through the consideration of both information imprecision and uncertainty. In the same time, the obtained benefit, when a prior knowledge source is injected in a fusion based decision system, can be quantified. By considering that, the fuzzyfication stage is optimized relatively to a given criteria using a genetic algorithm. By this manner, fuzzy sets corresponding to the physicians ambiguous a prior are defined objectively. At this level, we successively compare performances obtained when fuzzy functions are defined empirically and when they are optimized. We conclude this paper with the first results on esophagus wall segmentation and outline some further works.

Citation Download Citation

Renaud Debon, Basel Solaiman, Jean-Michel Cauvin, Michel Robaszkiewicz, and Christian Roux "Fuzzy propositions weighted by veracities or how to relate fuzzy logic and probability theory for segmentation of ultrasound images", Proc. SPIE 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV, (12 March 2002); https://doi.org/10.1117/12.460244

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Fuzzy logic

Esophagus

Image segmentation

Ultrasonography

Genetic algorithms

Data fusion

Genetics

Show All Keywords

Keywords/Phrases

Search In:

Publication Years