Accurate volume measurements of pituitary adenoma are important to the diagnosis and treatment for this kind of sellar
tumor. The pituitary adenomas have different pathological representations and various shapes. Particularly, in the case of
infiltrating to surrounding soft tissues, they present similar intensities and indistinct boundary in T1-weighted (T1W)
magnetic resonance (MR) images. Then the extraction of pituitary adenoma from MR images is still a challenging task.
In this paper, we propose an interactive method to segment the pituitary adenoma from brain MR data, by combining
graph cuts based active contour model (GCACM) and random walk algorithm. By using the GCACM method, the
segmentation task is formulated as an energy minimization problem by a hybrid active contour model (ACM), and then
the problem is solved by the graph cuts method. The region-based term in the hybrid ACM considers the local image
intensities as described by Gaussian distributions with different means and variances, expressed as maximum a posteriori
probability (MAP). Random walk is utilized as an initialization tool to provide initialized surface for GCACM. The
proposed method is evaluated on the three-dimensional (3-D) T1W MR data of 23 patients and compared with the
standard graph cuts method, the random walk method, the hybrid ACM method, a GCACM method which considers
global mean intensity in region forces, and a competitive region-growing based GrowCut method planted in 3D Slicer.
Based on the experimental results, the proposed method is superior to those methods.