Applications of Fuzzy Rule-based Systems in Medical Image Understanding - Rough Fuzzy Image Analysis: Foundations and Methodologies - page 97

6

Applications of Fuzzy Rule-based
Systems in Medical Image
Understanding

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.2 Fundamentals of fuzzy rule-based classification

system

6-2

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6.3 Fuzzy rule generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-4

Fuzzy rule generation with grade of certainty and
weighted input patterns • Weighted fuzzy classification
• Weighted fuzzy classifier with integrated learning •
Optimisation of fuzzy rules base • Fuzzy rule
generation with clustering and learning by examples •
Fuzzy clustering of the input feature space •
Generation of continuous membership functions and
class labeling • Generation and optimisation of fuzzy
rule base by learning from examples

Wojciech Tarnawski
Wroclaw University of Technology, Poland

Gerald Schaefer
School of Engineering & Applied Science,
Aston University, U.K.

6.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-16

Tomoharu Nakashima
College of Engineering, Osaka Prefecture
University, Japan

Breast cancer diagnosis based on histopathology •
Breast cancer classification based on thermograms •
Diagnosis of precancerous and cancerous lesions in
contact laryngoscopy

6.5 Conclusions 6-25

Lukasz Miroslaw
Wroclaw University of Technology, Poland

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Bibliography 6-29

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6.1 Introduction

Image understanding is a process representing the complex interaction between a computer
vision system and one or more digital images. According to (Tsotos, 1987), given a goal
or a reason for looking at a particular scene, image understanding system should produce
descriptions of both the images and the world scenes that the images represent. In terms
of medical imaging, image undertanding should lead to a successful interpretation of the
images and ideally contribute to an accurate diagnosis. Recent research aimed at machine
learning methods to develop strategies with the use of ad-hoc knowledge about the analysed
images and their context. Among them, one of the most promising approaches to explain
human image understanding is rule-based symbolic processing. Here, rules are extracted
through learning from examples or directly from expert knowledge. Many image under-
standing applications involve tasks such as image segmentation and edge detection that
extract significant information from an image which then often represents the input to a

6-1