Active contour model driven by optimized energy functionals for MR brain tumor segmentation with intensity inhomogeneity correction

Abstract

Segmentation of brain tumors is important for medical diagnosis, treatment planning, and disease development. However, the presence of image artifacts such as noise, intensity inhomogeneity, and partial volume effect in real-world images can aggressively affect the segmentation task. The manual segmentation of the tumor is highly error-prone and is a time-consuming task. Hence, in this work, we propose a methodology that can identify and segment the brain tumor slices in magnetic resonance (MRI) images. The work is composed of three main stages namely pre-processing, segmentation, and post-processing. In the pre-processing stage, N4ITK is applied to correct the intensity inhomogeneity and an Anisotropic diffusion filter is used to remove the noise present in MR images. In the segmentation stage, an active contour model that combines the region scalable fitting energy (RSF) and optimized laplacian of gaussian energy (OLoG) is proposed to segment the tumor region from MRI. The proposed segmentation method first presents an LoG energy term optimized by an energy functional that can smooth the homogeneous regions and enhance edge information simultaneously. Next, the optimized LoG energy term is combined with the RSF energy term which utilizes the local region information to drive the curve towards the boundaries. With the addition of the LoG term, the proposed model is insensitive to the positions of the initial contour and produces an accurate segmentation result. Finally, morphological operations and thresholding are used to extract the tumor region from the segmented image which is computed previously. The performance of the proposed segmentation method is evaluated using the BRATS, and J.Cheng dataset. The obtained experimental results demonstrate that our proposed method significantly outperforms the manual process and state of the art methods in brain tumor segmentation.