  In the field of biomedical imaging, ultrasonography has become increasingly
widespread, and an important auxiliary diagnostic tool with unique advantages,
such as being non-ionising and often portable. This article reviews the
state-of-the-art in medical ultrasound image computing and in particular its
application in the examination of the lungs. First, we review the current
developments in medical ultrasound technology. We then focus on the
characteristics of lung ultrasonography and on its ability to diagnose a
variety of diseases through the identification of various artefacts. We review
medical ultrasound image processing methods by splitting them into two
categories: (1) traditional model-based methods, and (2) data driven methods.
For the former, we consider inverse problem based methods by focusing in
particular on ultrasound image despeckling, deconvolution, and line artefacts
detection. Among the data-driven approaches, we discuss various works based on
deep/machine learning, which include various effective network architectures
implementing supervised, weakly supervised and unsupervised learning.
