Abstract
This study proposes a new liver segmentation method based on a sparse a priori statistical shape model (SP-SSM). First, mark points are selected in the liver a priori model and the original image. Then, the a priori shape and its mark points are used to obtain a dictionary for the liver boundary information. Second, the sparse coefficient is calculated based on the correspondence between mark points in the original image and those in the a priori model, and then the sparse statistical model is established by combining the sparse coefficients and the dictionary. Finally, the intensity energy and boundary energy models are built based on the intensity information and the specific boundary information of the original image. Then, the sparse matching constraint model is established based on the sparse coding theory. These models jointly drive the iterative deformation of the sparse statistical model to approximate and accurately extract the liver boundaries. This method can solve the problems of deformation model initialization and a priori method accuracy using the sparse dictionary. The SP-SSM can achieve a mean overlap error of 4.8% and a mean volume difference of 1.8%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 0.8 mm and 1.4 mm, respectively.
Highlights
The liver is the largest digestive gland and detoxification organ in the human body, and this vital organ produces bile
This paper proposes a new method called the Sparse A Priori Statistical Shape Model (SP-SSM), which is based on grayscale images to be segmented and the specificity of the border, and these features are used to build the energy model
This study proposes a statistical shape model based on sparse coding
Summary
The liver is the largest digestive gland and detoxification organ in the human body, and this vital organ produces bile. The integration of multiple functions makes the liver one of the major organs most prone to tumors. Liver cancer has become the second most common cause of cancer deaths worldwide. The prevention and treatment of liver diseases have been a focus worldwide. Computed tomography (CT) imaging can be used to acquire high-resolution hepatic anatomical structures. They can be found on the CT image by their characteristic inhomogeneous intensity distribution and unsmooth edges. CT imaging has represented of the most important imaging techniques for the diagnosis and treatment of clinical liver diseases.
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