To extract liver from medical images is a challenging task due to similar intensity values of liver with adjacent organs, various contrast levels, various noise associated with medical images and irregular shape of liver. To address these issues, it is important to preprocess the medical images, i.e., computerized tomography (CT) and magnetic resonance imaging (MRI) data prior to liver analysis and quantification. This paper investigates the impact of permutation of various preprocessing techniques for CT images, on the automated liver segmentation using deep learning, i.e., U-Net architecture. The study focuses on Hounsfield Unit (HU) windowing, contrast limited adaptive histogram equalization (CLAHE), z-score normalization, median filtering and Block-Matching and 3D (BM3D) filtering. The segmented results show that combination of three techniques; HU-windowing, median filtering and z-score normalization achieve optimal performance with Dice coefficient of 96.93%, 90.77% and 90.84% for training, validation and testing respectively.

翻译：从医疗图像中提取肝脏是一项艰巨的任务,原因是肝脏与相邻器官的密度值相似,各种对比水平不同,与医疗图像有关的各种噪音和肝脏的不正常形状。为了解决这些问题,必须在肝脏分析和量化之前预先处理医疗图像,即计算机断层成像和磁共振成像数据。本文调查了各种CT图像预处理技术的变异对自动肝脏分离的影响,利用深层学习,即U-Net结构,研究的重点是Hounsfield 单元(HU)窗口、有限的适应性直方图均匀化(CLACHE)、Z-芯片正常化、中位过滤和集成和3D(BM3D)过滤。分部分结果显示三种技术的组合;HU-风、中位过滤和z-c-core正常化分别实现了96.93%、90.77%和90.84%用于培训、验证和测试的Dice系数的最佳性表现。