Design of a Fuzzy Inference Based Ultrasound Image Analysis System for Differential Diagnosis of Thyroid Nodules

DOI

10.2991/ahis.k.210913.034How to use a DOI?

Keywords

Classification, Feature Selection, Fuzzy inferencing, Medical Image processing, Thyroid nodule

Abstract

This paper presents a Fuzzy Inference based Ultrasound Image Analysis System for differential diagnosis of Thyroid Nodules (TNs). Thyroid Ultrasound (TUS) images containing TNs are preprocessed to remove speckle noise and are enhanced using histogram equalization method. Nodule boundaries are identified using the canny edge detection technique and required Region of Interest is obtained using Adaptive Regularized Kernel Fuzzy C-means (ARKFCM) segmentation algorithm. Nineteen texture features are extracted from the segmented images. Best First (BF), Genetic Search (GS) and Greedy Step Wise (GSW) search methods are applied to select best subset of features. Selected features are fuzzified. A novel, fuzzy system is built to discriminate benign from malignant TNs by employing Mamdani model to draw inferences and centroid scheme for defuzzification. Class Based Association (CBA) concept is used to generate fuzzy inference rules. The developed multiple input, single output FIUIAS resulted in an accuracy of 98%.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - D Poornima
AU  - Karegowda Asha Gowda
AU  - K.R Pushpalatha
PY  - 2021
DA  - 2021/09/13
TI  - Design of a Fuzzy Inference Based Ultrasound Image Analysis System for Differential Diagnosis of Thyroid Nodules
BT  - Proceedings of the 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021)
PB  - Atlantis Press
SP  - 269
EP  - 277
SN  - 2589-4900
UR  - https://doi.org/10.2991/ahis.k.210913.034
DO  - 10.2991/ahis.k.210913.034
ID  - Poornima2021
ER  -