Wavelet-based computationally-efficient computer-aided characterization of liver steatosis using conventional B-mode ultrasound images. (July 2019) - Amin, Manar N.; Rushdi, Muhammad A.; Marzaban, Raghda N.; Yosry, Ayman; Kim, Kang; Mahmoud, Ahmed M.
Highlights: This work develops a computationally-efficient technique to classify fatty livers using conventional B-mode ultrasound images. The technique relies on extracting features from the Wavelet domain using the approximation part of ultrasound images. The technique was tested ex vivo on mice livers using two different datasets and in vivo on human livers using different ultrasound machines. This technique shall improve the implementation of manufacturer-independent real-time techniques for fatty liver classification. Abstract: Hepatic steatosis occurs when lipids accumulate in the liver leading to steatohepatitis, which can evolve into cirrhosis and consequently may end with hepatocellular carcinoma. Several automatic classification algorithms have been proposed to detect liver diseases. However, some algorithms are manufacturer-dependent, while others require extensive calculations and consequently prolonged computational time. This may limit the development of real-time and manufacturer-independent computer-aided detection of liver steatosis. This work demonstrates the feasibility of a computationally-efficient and manufacturer-independent wavelet-based computer-aided liver steatosis detection system using conventional B-mode ultrasound (US) imaging. Seven features were extracted from the approximation part of the second-level wavelet packet transform (WPT) of US images. The proposed technique was tested on two datasets of ex-vivo mice livers with and without gelatin embedding, in addition to a third dataset of in-vivo human livers acquired using two different US machines. Using the gelatin-embedded mice liver dataset, the technique exhibited 98.8% accuracy, 97.8% sensitivity, and 100% specificity, and the frame classification time was reduced from0.4814s using original US images to 0.1444s after WPT preprocessing. When the other mice liver dataset was used, the technique showed 85.74% accuracy, 84.4% sensitivity, and 88.5% specificity, and the frame classification time was reduced from 0.5612s to 0.2903s. Using human liver image data, the best classifier exhibited 92.5% accuracy, 93.0% sensitivity, 91.0% specificity, and the classification time was reduced from 0.660s to 0.146s. This technique can be useful for developing computationally-efficient and manufacturer-independent noninvasive CAD systems for fatty liver detection.