Segmentation of Brain MR Images Using a Charged Fluid Model

• Published in: IEEE transactions on biomedical engineering 2007-10, Vol.54 (10), p.1798-1813
• Main Authors: Chang, Herng-Hua, Valentino, Daniel J., Duckwiler, Gary R., Toga, Arthur W.
• Format: Article
• Language: English
• Subjects: Brain - pathology; Brain modeling; Brain Neoplasms - diagnosis; Charged fluid model (CFM); Computer Simulation; Deformable models; electrostatic equilibrium; fast Fourier transform (FFT); finite-size particles (FSP); Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image segmentation; Magnetic liquids; Magnetic resonance; Magnetic resonance imaging; magnetic resonance imaging (MRI); Magnetic Resonance Imaging - methods; Medical treatment; Models, Neurological; Neuroimaging; Poisson equations; Poisson's equation; Radiology; Rheology - methods; segmentation; Shape; Static Electricity
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2007.895104

In this paper, we developed a new deformable model, the charged fluid model (CFM), that uses the simulation of a charged fluid to segment anatomic structures in magnetic resonance (MR) images of the brain. Conceptually, the charged fluid behaves like a liquid such that it flows through and around different obstacles. The simulation evolves in two steps governed by Poisson's equation. The first step distributes the elements of the charged fluid within the propagating interface until an electrostatic equilibrium is achieved. The second step advances the propagating front of the charged fluid such that it deforms into a new shape in response to the image gradient. This approach required no prior knowledge of anatomic structures, required the use of only one parameter, and provided subpixel precision in the region of interest. We demonstrated the performance of this new algorithm in the segmentation of anatomic structures on simulated and real brain MR images of different subjects. The CFM was compared to the level-set-based methods [Caselles et al. (1993) and Malladi etal. (1995)] in segmenting difficult objects in a variety of brain MR images. The experimental results in different types of MR images indicate that the CFM algorithm achieves good segmentation results and is of potential value in brain image processing applications.