Ultrasound tissue characterization based on the Lempel–Ziv complexity with application to breast lesion classification. (May 2019)

• Record Type: Journal Article
• Authors: Steifer, Tomasz; Lewandowski, Marcin
• Is Part Of: Biomedical signal processing and control. Volume 51(2019)
• Page Start: 235
• Page End: 242
• Publication Date: 2019-05
• Subjects: Quantitative ultrasound -- Tissue characterization -- Speckles -- Breast cancer -- Lempel–Ziv complexity -- Applied information theory -- Space-filling curves; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.02.020
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A method based on Lempel–Ziv complexity is proposed for quantitative ultrasound tissue characterization. The method is used to classify breast lesions from an open access ultrasound image database. The method performance is compared with entropy-based classifier as entropy is related theoretically to Lempel–Ziv complexity. The new method achieves 0.87 ROC AUC as compared to 0.84 achieved by the reference method. Abstract: Building upon the recent successes in the application of information-theoretic concepts (e.g. Shannon entropy) in quantitative ultrasound, the authors propose a novel tissue characterization method based on the Lempel–Ziv complexity. In this procedure, standard ultrasound B-Mode images are mapped onto words over finite alphabets before the corresponding Lempel–Ziv complexity of ultrasound images is calculated. Such complexity metric may be used to differentiate between types of tissues. Here, the method is utilized as a binary classifier for the malignancy of breast lesions. The method is tested on OASBUD – an open-access breast lesions image database. Images of 48 malignant and 48 benign lesions were used – two images for each lesion. The new procedure slightly outperforms the state-of-art classifier based on pixel entropy as measured in the size of area under the receiver operating curve (ROC AUC), which suggests that it may serve as a basis for computer-assisted breast cancer ultrasound diagnosis and possibly in other standard applications of the quantitative ultrasound.