Evaluation of No-reference quality metrics for Ultrasound liver images

M. Outtas, Lu Zhang, O. Déforges, W. Hamidouche, A. Serir
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引用次数: 6

Abstract

Although assessing post-processed medical images is still done by radiologists (rather than computers), numerous algorithms dedicated to medical image processing are developed without taking into consideration the expert's perceived quality scores. In order to evaluate these algorithms, we study in this paper four No-Reference(NR) quality assessment metrics in terms of correlation with perceived scores of experts. These scores were obtained through subjective tests conducted on ultrasound (US) livers images. Results show that one NR metric among the four evaluated performs the best for assessing the quality of US images. However, further study is needed for the development of more suitable NR metrics.
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肝脏超声图像无参考质量指标的评价
尽管评估后处理的医学图像仍然是由放射科医生(而不是计算机)完成的,但许多专门用于医学图像处理的算法都没有考虑到专家的感知质量分数。为了评估这些算法,我们研究了四种无参考(NR)质量评估指标与专家感知得分的相关性。这些分数是通过对超声(US)肝脏图像进行主观测试获得的。结果表明,在评估的四个NR指标中,一个NR指标对评估美国图像的质量表现最好。然而,需要进一步的研究来制定更合适的NR指标。
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