Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging

Abstract

Diffuse liver diseases, such as hepatitis, fatty liver, and cirrhosis, are becoming a leading cause of fatality and disability all over the world. Early detection and diagnosis of these diseases is extremely important to save lives and improve effectiveness of treatment. Ultrasound imaging, a noninvasive diagnostic technique, is the most commonly used modality for examining liver abnormalities. However, the accuracy of ultrasound-based diagnosis depends highly on expertise of radiologists. Computer-aided diagnosis systems based on ultrasound imaging assist in fast diagnosis, provide a reliable "second opinion" for experts, and act as an effective tool to measure response of treatment on patients undergoing clinical trials. In this review, we first describe appearance of liver abnormalities in ultrasound images and state the practical issues encountered in characterization of diffuse liver diseases that can be addressed by software algorithms. We then discuss computer-aided diagnosis in general with features and classifiers relevant to diffuse liver diseases. In later sections of this paper, we review the published studies and describe the key findings of those studies. A concise tabular summary comparing image database, features extraction, feature selection, and classification algorithms presented in the published studies is also exhibited. Finally, we conclude with a summary of key findings and directions for further improvements in the areas of accuracy and objectiveness of computer-aided diagnosis.

Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images

“…It is operator independent and does not require ROI selection in comparison with the HI method and the GLCM-based approach. Next, the proposed method efficiently utilizes sequences of US images to assess the level of steatosis, while the approaches proposed in the literature commonly employs only one US image to conduct classification [ 6 ]. However, there are several issues related to our work.…”

“…Up to date various approaches have been proposed to assess the level of steatosis in liver using US [ 6 ]. Among them, the hepatorenal sonographic index (HI) is considered to be highly efficient and simple [ 7 , 8 ].…”

“…Another approach to liver steatosis assessment employs texture analysis. According to the review paper on liver image analysis [ 6 ], the gray-level co-occurrence matrix (GLCM) algorithm is the most frequently used method for liver disease characterization [ 11 ]. GLCMs provide useful information about spatial gray-level dependencies in an image.…”

Quantitative Ultrasound and B-Mode Image Texture Features Correlate with Collagen and Myelin Content in Human Ulnar Nerve Fascicles

“…Image texture analysis was performed using the GLCM algorithm (Haralick, et al 1973), a frequently employed technique in US imaging (Andrade, et al 2012, Bharti, et al 2016, Flores, et al 2015, Gaitini, et al 2004, Sadeghi-Naini, et al 2017. GLCM contains co-occurrence probabilities of all pair wise combinations of grey levels in an image or image region.…”

Rician Beamforming: Despeckle Method via Coarray Projection Stochastic Analysis

“…Leading to contrast ratio lowering and aberrations depending on the point of view of the aperture and the structure geometry itself [2]. These hinder the processing of ultrasonic images from segmentation [3] and registering [4], required for Computer Aided Detection (CAD) processing chains [5][6][7].…”