表征肝肿瘤仍然是临床实践中的一个挑战。与B型成像相比，基于统计分布的超声参数成像可以增强图像对比度，需要散射体遵循特定的分布。本研究提出了一种基于像素的小窗口参数超声成像方法，使用加权水平归一化香农熵（WhNSE）和模糊熵（FE）来提高肝脏肿瘤的可检测性。基于像素的参数成像需要一个滑动窗口来遍历B-以一个像素为步长的模式图像，同时通过窗口中的像素值计算熵。熵被分配给滑动窗口的中心像素。得到所有像素的熵值后就得到了熵图像。 FE 和 WhNSE 是首先应用于参数成像的两种新颖的熵。通过模拟和临床探索评估感兴趣区域（ROI）和对比度噪声比（CNR）的检测能力。在模拟中，FE成像在检测高回声ROI方面表现出最高的改进，CNR增益高达457.31模拟中的% (p < 0.01)。 WhNSE成像在高回声ROI检测中表现出最佳性能，CNR为1.607 ± 0.816（p = 0.05），显着高于B模式图像。提出的基于模糊熵和加权水平归一化香农熵的基于像素的参数成像方法两者都有效增强了超声图像的对比度和可检测性。由于考虑了相邻像素的关系，采用适当参数的基于像素的模糊熵成像的成像增强方法获得了更好的检测性能。© Korean Society of Medical and Biological Engineering 2024。Springer Nature 或其许可方（例如一个协会）或其他合作伙伴）根据与作者或其他权利持有人的出版协议拥有本文的专有权；作者对本文已接受的手稿版本的自行存档仅受此类出版协议和适用法律的条款的约束。

Characterizing liver tumors remains a challenge in clinical practice. Ultrasound parametric imaging based on statistical distribution can enhance image contrast compared with B-mode imaging, requiring scatterers following specific distributions. This study proposes a pixel-based small-window parametric ultrasound imaging method using weighted horizontally normalized Shannon entropy (WhNSE) and fuzzy entropy (FE) to improve detectability liver tumor.Pixel-based parametric imaging requires a sliding window to traverse across the B-mode image with the step of one pixel, while calculating the entropy by the pixel values in the window. The entropy is assigned to the center pixel of the sliding window. The entropy image is obtained after getting the entropy values of all pixels. FE and WhNSE are two novel entropies first applied to parametric imaging. The detection abilities of regions of interest (ROI) and the contrast-to-noise ratio (CNR) were evaluated through simulations and clinical explorations.In simulations, FE imaging showed the highest improvement in detecting hyperechoic ROIs, with a CNR gain up to 457.31% (p < 0.01) in simulations. WhNSE imaging demonstrated the best performance in hyperechoic ROI detection, with a CNR of 1.607 ± 0.816 (p = 0.05), significantly higher than B-mode images.The proposed pixel-based parametric imaging method based on fuzzy entropy and weighted horizontally normalized Shannon entropy both effectively enhance the contrast and detectability of ultrasound images. The imaging enhancement method of the pixel-based fuzzy entropy imaging with proper parameters got better detection performance, due to the consideration of the relationship of neighboring pixels.© Korean Society of Medical and Biological Engineering 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.