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

• Published in: Computers in biology and medicine Vol. 94; pp. 11 - 18
• Main Authors: Acharya, U. Rajendra, Koh, Joel En Wei, Hagiwara, Yuki, Tan, Jen Hong, Gertych, Arkadiusz, Vijayananthan, Anushya, Yaakup, Nur Adura, Abdullah, Basri Johan Jeet, Bin Mohd Fabell, Mohd Kamil, Yeong, Chai Hong
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2018; Elsevier Limited
• Subjects: Adult; Automation; Benign; Cirrhosis; Classification; Computer-aided diagnostic system; Decomposition; Diagnosis; Diagnosis, Computer-Assisted - methods; Discriminant analysis; Fatty liver; Feature extraction; Female; Human performance; Humans; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Internal Medicine; International conferences; Learning algorithms; Lesions; Liver; Liver cancer; Liver cirrhosis; Liver Cirrhosis - diagnosis; Liver Cirrhosis - diagnostic imaging; Liver diseases; Liver lesions; Liver Neoplasms - diagnosis; Liver Neoplasms - diagnostic imaging; Machine Learning; Male; Malignant; Medical diagnosis; Middle Aged; Neural networks; Other; Particle swarm optimization; Principal components analysis; Radon; Radon transformation; Ultrasonic imaging; Ultrasonography; Ultrasound; Variance analysis; Malaysia; Computer-aided diagnostic system; Malignant; Liver lesions; Benign; Machine learning; Ultrasonography
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2017.12.024

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. [Display omitted] •Classification of normal, benign and malignant liver images.•Bidirectional empirical mode decomposition performed.•Particle swarm optimization is used for feature selection.•Obtained accuracy of 92.95% using 29 features with PNN classifier.