Model creation and deformation for the automatic segmentation of the brain in MR images

Abstract

In this paper a method for the automatic segmentation of the brain in magnetic resonance images is presented and validated. The proposed method involves two steps 1) the creation of an initial model and 2) the deformation of this model to fit the exact contours of the brain in the images. A new method to create the initial model has been developed and compared to a more traditional approach in which initial models are created by means of brain atlases. A comprehensive validation of the complete segmentation method has been conducted on a series of three-dimensional T1-weighted magnetization-prepared rapid gradient echo image volumes acquired both from control volunteers and patients suffering from Cushing's disease. This validation study compares results obtained with the method we propose and contours drawn manually. Averages differences between manual and automatic segmentation with the model creation method we propose are 1.7% and 2.7% for the control volunteers and the Cushing's patients, respectively. These numbers are 1.8% and 5.6% when the atlas-based method is used.

Methods on Skull Stripping of MRI Head Scan Images—a Review

“…Aboutanos et al [60] evolved a 2D contour to find the brain border in T1-weighted image by maximizing its corresponding one-dimensional (1D) optimization problem, which was obtained via geometrical transformation from a 2D contour using dynamic programming techniques. The 1D optimization problem was described by a cost function that consists of intensity value, morphology, gradient, the moving speed of the contour, and the smoothness of the contour.…”

Skull-stripping magnetic resonance brain images using a model-based level set

“…Several skull-stripping algorithms have been proposed in the literature (Lee et al, 2000;Huh et al, 2000;Shattuck et al, 2001;Aboutanos et al, 1999;Zeng et al, 1999;Dale et al, 1999;Suri, 2001;Smith, 2002;Baillard et al, 2001), and a number of recent studies have compared the accuracy and performance characteristics of these techniques (Fennema-Notestime et al, 2006;Boesen et al, 2004;Rehm et al, 2004). Many of the existing techniques can be tuned to skull-strip a given data set, but none of these techniques can automatically skull-strip the variety of data sets contained in the largescale studies that increasingly characterize contemporary neuroimage analyses.…”

“…Aboutanos et al (1999) evolved a 2D contour by maximizing its corresponding 1D optimization problem, which was obtained via geometrical transformation from a 2D contour using dynamic programming techniques. The 1D optimization problem was described by a cost function that consisted of six terms including intensity value, morphology, gradient, moving speed of the contour, and smoothness of the contour.…”

Untitled

“…The original active contour method, the snake, and its variations [6 -11] are disposed to large error results when dealing with "false" edges and noisy images. Several implementations, such as the minimal path technique by Cohen et al [12][13] or dual snakes [14], and other similar methods [15][16][17][18][19], have been suggested to correct the error associated with challenging images. Unfortunately, all of these classical snakes and active contour models can only detect objects with edges defined by the gradient, and, as expected, the performance of the totally edge based methods is often inadequate.…”