  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.
