A Digital Image Processing Approach for Hepatic Diseases Staging based on the Glisson's Capsule

TL;DR

This paper proposes a non-invasive ultrasound-based method combining image processing and neural networks to assess liver fibrosis by analyzing the Glisson's capsule surface, aiming for effective disease staging.

Contribution

It introduces a novel approach that leverages the smoothness of the Glisson's capsule surface in ultrasound images using combined image processing and CNN classifiers for liver disease staging.

Findings

Effective extraction of Glisson's capsule line from ultrasound images

Classifiers show potential in differentiating fibrosis stages

Preliminary results support further development of non-invasive staging methods

Abstract

Due to the need for quick and effective treatments for liver diseases, which are among the most common health problems in the world, staging fibrosis through non-invasive and economic methods has become of great importance. Taking inspiration from diagnostic laparoscopy, used in the past for hepatic diseases, in this paper ultrasound images of the liver are studied, focusing on a specific region of the organ where the Glisson's capsule is visible. In ultrasound images, the Glisson's capsule appears in the shape of a line which can be extracted via classical methods in literature. By making use of a combination of standard image processing techniques and Convolutional Neural Network approaches, the scope of this work is to give evidence to the idea that a great informative potential relies on smoothness of the Glisson's capsule surface. To this purpose, several classifiers are taken into consideration, which deal with different type of data, namely ultrasound images, binary images depicting the Glisson's line, and features vector extracted from the original image. This is a preliminary study that has been retrospectively conducted, based on the results of the elastosonography examination.