Quantitative Grading Using Grey Relational Analysis on Ultrasonographic Images of a Fatty Liver

Abstract

A quantitative graduation system based on Grey Relational Analysis is proposed to recognize fatty livers in B-scan ultrasonic images. We evaluated ultrasonography liver images from 95 subjects having fatty livers (Grade I, II, III) and 45 normal subjects, as diagnosed by an expert radiologist. In practice, ultrasonographical findings of fatty liver are based on the brightness level of the liver in comparison to the renal parenchyma. The development of a non-invasive and accurate method would be of great clinical value as an alternative to diagnosing fatty liver based on the radiologist's visual perception. In this study, we also evaluated AST and ALT liver enzymes for fatty liver having different grades. A high correlation between enzymes and Grey Relational Grades were found. The Receiver Operating Characteristic (ROC) curves were obtained and yielded satisfactory classification results using sensitivity, specificity and area under the curve for computing graduation and distinguishing fatty livers from healthy livers. With the proposed method based on Grey Relational Analysis, not only misdiagnosis caused by subjective differences in clinical evaluation will be reduced, but also the early diagnosis fatty liver and quantitative assessment of its degree will be achieved.

Fully automatic segmentation and classification of liver ultrasound images using completed LBP texture features

“…The most common causes of fatty liver are obesity, alcoholism, high blood triglycerides, diabetes and hepatitis [1,2]. In recent years, fatty liver disease has become one of the most serious disease threatens people's health.…”

“…Extracting information from the fatty liver ultrasound images are based on the changes of scanned image intensity. Fatty liver causes an increase of echogenicity in the liver tissue [1].…”

“…In order to find an approach to diagnose and detect automatically fatty liver disease and other diffused and focal liver diseases, some authors recently proposed methods and tools based on computer-aided diagnosis (CAD) to help the physicians and experts to detect and classify fatty liver from normal liver or heterogeneous one using ultrasound images [1,5,6]. Andrade et al [6] presented a semi-automatic classification approach to recognition fatty and normal liver.…”

Attribute reduction based on the grey relational analysis and dynamic programming

“…The grey relational grade is a weighted sum of the grey relational coefficient, where ij γ indicates the correlation magnitude measured between the reference sequence and the comparison sequences [15].…”

B-scan Image Feature Extraction of Fatty Liver

“…If one only uses grayscale characteristics, fatty liver can be well identified. Therefore, refining the B-image as multilevel gray scales is effective to reflect the near-by and far-field from fatty liver B super images [7]. Therefore, we hereby present out methodology to demonstrate how to conduct fatty liver B super image recognition.…”