Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features

Acharya, U. Rajendra and Koh, Joel En Wei and Hagiwara, Yuki and Tan, Jen Hong and Gertych, Arkadiusz and Vijayananthan, Anushya and Yaakup, Nur Adura and Abdullah, Basri Johan Jeet and Mohd Fabell, Mohd Kamil and Yeong, Chai Hong (2018) Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features. Computers in Biology and Medicine, 94. pp. 11-18. ISSN 0010-4825

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Official URL: https://doi.org/10.1016/j.compbiomed.2017.12.024

Abstract

Liver is the heaviest internal organ of the human body and performs many vital functions. Prolonged cirrhosis and fatty liver disease may lead to the formation of benign or malignant lesions in this organ, and an early and reliable evaluation of these conditions can improve treatment outcomes. Ultrasound imaging is a safe, non-invasive, and cost-effective way of diagnosing liver lesions. However, this technique has limited performance in determining the nature of the lesions. This study initiates a computer-aided diagnosis (CAD) system to aid radiologists in an objective and more reliable interpretation of ultrasound images of liver lesions. In this work, we have employed radon transform and bi-directional empirical mode decomposition (BEMD) to extract features from the focal liver lesions. After which, the extracted features were subjected to particle swarm optimization (PSO) technique for the selection of a set of optimized features for classification. Our automated CAD system can differentiate normal, malignant, and benign liver lesions using machine learning algorithms. It was trained using 78 normal, 26 benign and 36 malignant focal lesions of the liver. The accuracy, sensitivity, and specificity of lesion classification were 92.95%, 90.80%, and 97.44%, respectively. The proposed CAD system is fully automatic as no segmentation of region-of-interest (ROI) is required.

Item Type: Article
Uncontrolled Keywords: Computer-aided diagnostic system; Liver lesions; Benign; Malignant; Machine learning; Ultrasonography
Subjects: R Medicine
Divisions: Faculty of Engineering
Faculty of Medicine
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 08 Oct 2019 06:48
Last Modified: 08 Oct 2019 06:48
URI: http://eprints.um.edu.my/id/eprint/22708

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