@inproceedings{a01b3ab409ca4702aa6d67391ce06604,
title = "Noise resistance analysis of wavelet-based channel energy feature for breast lesion classification on ultrasound images",
abstract = "Wavelet-based channel energy with low cost and high efficacy is a valuable feature for the differential diagnoses between benign and malignant breast lesions. The new feature is a contour approach that generally suffers from lacking a reliable contour detection algorithm with convincing results due to extreme noise. For investigating a procedure suitable for clinical application, noise resistance capability of the new feature is evaluated in this study. The evaluation system consists of two snake-based contour detection algorithms associated with two pre-processes. These combinations can produce four test datasets of contour sonogram. Classification performance evaluation is based on a probabilistic neural network and a genetic algorithm used for distribution parameter determination.",
author = "Liao, {Yueh Ching} and Guo, {Shu Mei} and Hung, {King Chu} and Wang, {Po Chin} and Yang, {Tsung Lung}",
year = "2010",
doi = "10.1007/978-3-642-15696-0_51",
language = "English",
isbn = "3642156959",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "549--558",
booktitle = "Advances in Multimedia Information Processing, PCM 2010 - 11th Pacific Rim Conference on Multimedia, Proceedings",
edition = "PART 2",
note = "11th Pacific Rim Conference on Multimedia, PCM 2010 ; Conference date: 21-09-2010 Through 24-09-2010",
}