Abstract
Automatic segmentation and three-dimensional reconstruction of the liver is important for liver disease diagnosis and surgical treatment. However, the shape of the imaged 2D liver in each CT image changes dramatically across the slices. In all slices, the imaged 2D liver is connected with other organs, and the connected organs also vary across the slices. In many slices, the intensities of the connected organs are the same with that of the liver. All these facts make automatic segmentation of the liver in the CT image an extremely difficult task. In this paper, we propose a heuristic approach to segment the liver automatically based on multiple thresholds. The thresholds are computed based on the slope difference distribution that has been proposed and verified in the previous research. Different organs in the CT image are segmented with the automatically computed thresholds, respectively. Then, different segmentation results are combined to delineate the boundary of the liver robustly. After the boundaries of the 2D liver in all the slices are identified, they are combined to form the 3D shape of the liver with a global energy minimization function. Experimental results verified the effectiveness of all the proposed image processing algorithms in automatic and robust segmentation of the liver in CT images.
Highlights
Liver diseases have become one of the most common causes of deaths in the world
In recent years, computed tomography (CT) imaging has been widely used in liver disease diagnosis and surgical treatment because tumors or hepatic lesions could be observed from the CT image
For the captured CT images, the liver slices need to be examined in the two dimensions one by one
Summary
Liver diseases have become one of the most common causes of deaths in the world. Researchers have focus on the prevention and treatment of liver diseases for many years. There are many situations in which these small blobs are connected with the liver or the segmented organ, which make the removal of them more difficult To remove all these interference blobs, we propose a morphological filtering method that contains the following steps. E centroids of these blobs are Figure 3: Demonstration of the proposed liver segmentation method with a typical image: (a) the segmented liver after energy minimization; (b) the segmented liver after morphological filtering; (c) the segmented liver after morphological area filtering; (d) the calculated liver boundary overlaying on the original image. With the constraint of fitted curve Icurve, the circular part Icir, and the merged stomach Istm, the liver is segmented by the threshold T4 as follows:. Where Pf denotes the smoothed point and P denotes the original sampled point. α is a smoothing factor and f is the fitted spline function (m 1, 2, . . . , Nstack, n 1, 2, . . . , 200)
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