Ultrasound tissue classification: a review

Abstract

Ultrasound imaging is the most widespread medical imaging modality for creating images of the human body in clinical practice. Tissue classification in ultrasound has been established as one of the most active research areas, driven by many important clinical applications. In this paper, we present a survey on ultrasound tissue classification, focusing on recent advances in this area. We start with a brief review on the main clinical applications. We then introduce the traditional approaches, where the existing research on feature extraction and classifier design are reviewed. As deep learning approaches becoming popular for medical image analysis, the recent deep learning methods for tissue classification are also introduced. We briefly discuss the FDA-cleared techniques being used clinically. We conclude with the discussion on the challenges and research focus in future.

Room‐Temperature‐Processable Highly Reliable Resistive Switching Memory with Reconfigurability for Neuromorphic Computing and Ultrasonic Tissue Classification

“…Ultrasonic images or RF signals obtained from the tissues can provide valuable information addressing unmet clinical needs, but the ultrasonic modalities rely on raw waveforms and images suffering from a limited signal-to-noise ratio (SNR) or contrast-to-noise ratio (CNR), respectively. In an effort to overcome such limitations involved in the measurement step and thus eventually utilize for clinical diagnosis, not just for an initial screening tool, deep learning-based approaches have been actively utilized for ultrasonic tissue classification with convolutional neural network (CNN) [88,89] and recurrent neural network (RNN). [90] These allow automated computation of temporal or spectral features of raw signals in a self-taught manner and thus increase an accuracy rate for tissue classification.…”

Photoacoustic Imaging in Medicine – A Review

“…Breast cancer is the second common cancer worldwide after lung cancer, the fifth common cause of cancer death [97,98]. X-ray-based Mammography [97], Breast ultrasound (BUS) imaging [99][100][101], and Magnetic resonance imaging (MRI) [102,103], are the three most famous imaging techniques utilized for breast cancer screening. X-ray-based screening mammography, beginning in the 1980s, helps in the early detection of breast cancer [97].…”

Fully‐automatic identification of gynaecological abnormality using a new adaptive frequency filter and histogram of oriented gradients (HOG)

“…Ultrasound tissue classification has been identified as one of the most active fields of clinical study, powered by several major clinical applications (Shan et al, 2020). Since the relationship between acoustic wave and biological tissue (an inhomogeneous medium) is too hard for modelling, the classification of ultrasound tissue is complicated.…”

“…While the quality of ultrasound images with regards to signal‐to‐noise and contrast‐to‐noise rates has enhanced dramatically in the modern years, the challenges in classifying various tissue types in US images remain intact. Ultrasound image detection is a medical diagnosis method that utilizes dispersed or reflected ultrasound echo data to identify lesion regions in the human body, depending on the variation in acoustic impedance of various human tissues (Shan et al, 2020). Compared to the conventional diagnosis, it substantially boosts the objectivity and precision of diagnosis.…”