Table of Contents
ISRN Corrosion
Volume 2013, Article ID 376823, 7 pages
Research Article

Evaluation of Corrosion Growth on SS304 Based on Textural and Color Features from Image Analysis

Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, 401 West Main Street, Richmond, VA 23284, USA

Received 2 July 2013; Accepted 18 August 2013

Academic Editors: W. Fürbeth and R. Salghi

Copyright © 2013 Ramana M. Pidaparti et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Corrosion surface damage in the form of pitting and microcracks is observed in many systems and affects the integrity of steel structures in nuclear, civil, and industrial engineering. In order to gain a better understanding and develop nondestructive and automatic detection/assessment of corrosion damage and its growth, an image analysis based on texture using wavelet transforms and color features was carried out. Experiments were conducted on steel 304 panels under three different electrolyte solutions, and periodic scans were used to obtain the images for analysis over time. The results obtained from the image analysis are presented to illustrate the metrics which best characterize early stage corrosion damage growth behavior. The results obtained indicate that textural features in combination with color features are more effective and may be used for correlating service/failure conditions based on corrosion morphology.