Automatic liver segmentation from abdominal computed tomography (CT) images based on gray levels or shape alone is
difficult because of the overlap in gray-level ranges and the variation in position and shape of the soft tissues. To address
these issues, we propose an automatic liver segmentation method that utilizes low-level features based on texture
information; this texture information is expected to be homogenous and consistent across multiple slices for the same
organ. Our proposed approach consists of the following steps: first, we perform pixel-level texture extraction; second, we
generate liver probability images using a binary classification approach; third, we apply a split-and-merge algorithm to
detect the seed set with the highest probability area; and fourth, we apply to the seed set a region growing algorithm
iteratively to refine the liver's boundary and get the final segmentation results. Furthermore, we compare the
segmentation results from three different texture extraction methods (Co-occurrence Matrices, Gabor filters, and Markov
Random Fields (MRF)) to find the texture method that generates the best liver segmentation. From our experimental
results, we found that the co-occurrence model led to the best segmentation, while the Gabor model led to the worst liver
segmentation. Moreover, co-occurrence texture features alone produced approximately the same segmentation results as
those produced when all the texture features from the combined co-occurrence, Gabor, and MRF models were used.
Therefore, in addition to providing an automatic model for liver segmentation, we also conclude that Haralick cooccurrence
texture features are the most significant texture characteristics in distinguishing the liver tissue in CT scans.