Recognition of liver steatosis degree based on multi-fractal spectrum of type-B ultrasonic image

VernacularTitle:基于B超图像多重分形谱的脂肪肝严重程度识别
Author: Jianwei ZHANG ; Tianfu WANG ; Jie YIN ; Xiaoxia ZHA ; Yan LUO
Publication Type:Journal Article
Keywords: Ultrasonography; Fatty liver; Multi-fractal spectrum; Computer-aided diagnosis; Artificial neural network
From: Chinese Journal of Medical Imaging Technology 2009;25(7):1289-1292
CountryChina
Language:Chinese
Abstract: Objective To develop a recognition method of liver steatosis degree on type-B ultrasonic images based on multi-fractal spectrum texture analysis method and pattern recognition. Methods Features of singularity strength width and multi-spectrum area were extracted from the curve of multi-fractal spectrum of each liver ultrasonic images. These two features and the feature of mean intensity ratio comprised a three-dimensional feature vector, which would be classified by BP neural network. Results The classification accuracy was 96.00% for normal liver, 80.00% for mild fatty liver, 88.00% for moderate fatty liver and 92.00% for severe fatty liver. Conclusion Feature vector combined with BP neural network can identify the steatosis degree of liver on the ultrasonic images and can be used as an assistant diagnostic method.