Quantitative assessment for characterization of breast lesion tissues using adaptively decomposed ultrasound RF images. (May 2022)

• Record Type: Journal Article
• Authors: Yao, Ruihan; Zhang, Yufeng; Wu, Keyan; Li, Zhiyao; He, Meng; Fengyue, Baoping
• Is Part Of: Biomedical signal processing and control. Volume 75(2022)
• Publication Date: 2022-05
• Subjects: Radio-frequency ultrasonic echo signal -- Fast multivariate empirical mode decomposition -- Small window entropy -- Intrinsic mode function image -- Breast lesion tissue characterization; 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.2022.103559
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The small-window entropy (SWE) based on adaptively decomposed ultrasound radio frequency (RF) images was proposed for the characterization of breast lesion tissues. A fast multivariate empirical mode decomposition algorithm was employed to adaptively decompose the preprocessed RF images for the SWE calculation. The SWEs of the certain components improved the performance for distinguishing between benign and malignant breast lesions. The proposed methods can serve as a basis for computer-assisted breast cancer ultrasound diagnosis. Abstract: Quantitative ultrasound is of significant relevance to the clinical diagnosis of benign and malignant breast lesions. Owing to desmoplastic reactions in the tumor and malignant cells invading the surrounding tissues, the irregular boundary of the lesion is a significant indication of malignancy. Complexity measured using entropy based on ultrasound radio frequency (RF) data is investigated to characterize the disease; however, the performance is reduced due to the slowly varying components in the data. In the present study, adoption of small-window entropy (SWE) based on adaptively decomposed ultrasound RF images is proposed for the characterization of breast lesion tissues. First, down-sampled, dilated and cropped RF (DDC_RF) images are obtained from the original ultrasonic RF data. Subsequently, a fast multivariate empirical mode decomposition algorithm is employed to adaptively decompose the DDC_RF images into intrinsic mode functions (IMFs). The ring regions of interest that are the areas surrounding the tumors are determined from all individual IMF images and their different combinations to calculate the SWEs. The assessment is performed using the Open Access Series of Breast Ultrasonic Data (OASBUD), which includes original RF data and histological assessment results of 48 benign and 48 malignant lesions. The receiver operating curve (ROC) and the area under the ROC (AUC) values are calculated to assess the ability to diagnose breast lesions. The results show that the SWEs based on the combinations of IMFs 1–3, IMFs 1, 2, IMFs 1–4, and IMFs 1, 3 provided the AUC values of 0.88, 0.87, 0.86, and 0.85, respectively, which are greater than 0.84, obtained from the undecomposed RF images. It is concluded that the SWE based on the adaptively decomposed ultrasound RF images is an improved characteristic for distinguishing between benign and malignant breast lesions, and it is suggested that it may serve as a basis for computer-assisted breast cancer ultrasound diagnosis.