Multiple ROI selection based focal liver lesion classification in ultrasound images

摘要

Ultrasound imaging is one of the most widely used imaging modality for the purpose of visualizing the human soft tissues. Especially, liver imaging application is of great importance in the areas of diagnostic ultrasound. In ultrasound liver image, the classification of lesions depends heavily on the characteristics of the lesions including internal echo, morphology, edge, echogenicity, and posterior echo enhancement. These characteristics are differently observed according to ROI selection methods that may indeed significantly impact the classification performances. Currently developed ROI selection methods have limitation for guaranteeing robust classification performance for focal liver lesions, mainly due to the inherent difficulties that represent all ultrasonic appearances of characteristics of lesion. In order to obtain better and more stable classification performances, we propose a new and novel approach, so-called multiple-ROI based focal liver lesion classification. The proposed approach properly combines the advantages of existing ROI selection methods to represent well various ultrasonic appearances of liver lesions including internal echo, morphology, edge, echogenicity, and posterior echo enhancement. To verify the effectiveness of the proposed ROI selection approach, extensive and comparative experiments have been performed using a total of 150 ultrasound images. Each ultrasound image contains one corresponding focal liver lesion so that a total of 150 focal liver lesions is used, comprising of 50 cysts, 50 hemangiomas, and 50 malignancies. Experimental results show that the proposed multiple-ROI-based approach can achieve the enhanced and stable classification performance regardless of features being used. In addition, our proposed method outperforms other existing classification methods designed for focal liver lesion classification. Especially, the proposed approach attains classification accuracy of up to 80% over well-known challenging task of classifying the hemangiomas and malignancies.