3D Tumor Segmentation from Volumetric Brain MR Images Using Level-Sets Method

1.1 Statement of the problem Segmentation in volumetric images is a tool allowing a diagnostics automation and as well will assist experts in quantitative and qualitative analysis. It’s an important step in various applications such as visualization, morphometrics and image-guided surgery. In the context of neuro-imaging, brain tumor segmentation from Magnetic Resonance Images (MRI) is extremely important for treatment planning, therapy monitoring, examining efficacy of radiation and drug treatments and studying the difference between healthy subjects and subjects with brain tumor. The task of manually segmentation of brain tumor from MR images is generally time-consuming and difficult. Anyway, the task is done by marking by hand the tumor regions slice-by-slice which generates set of jaggy images, so the practitioner is confronted with a succession of boundary which he mentally stacked up to be made a 3D shape of brain tumor. This shape is inevitably subjective and becomes infeasible when dealing with large data sets, also there is losing of information in the third dimension because is not taken into account in the segmentation process. All this, affect the quality and accuracy of clinical diagnosis. An automatic or semi-automatic segmentation method of brain tumor that takes entire information within the volumetric MR image into account is desirable as it reduces the load on the human raters and generates optimal segmented images (Wang & al., 2004), (Michael & al., 2001), (Lynn & al., 2001). Specially, automatic brain tumor segmentation presents many challenges and involves various disciplines such us pathology, MRI physics and image processing. Brain tumors are difficult to segment because they vary greatly in size and position, may be of any size, may have a variety of shapes and may have overlapping intensities with normal tissue and edema. This leads to numerous segmentation approaches of automatic brain tumor extraction. Low-level segmentation methods, such as pixel-based clustering, region growing, and filter-based edge detection, requires additional pre-processing and post-processing as well as considerable amounts of expert intervention and a priori knowledge on the regions of interest (ROI) (Sahoo & al., 1988). Recently, several attempts have been made to apply deformable models to brain image analysis (Moon & al., 2002). Indeed, deformable models refer to a large class of computer vision methods and have proved to be a successful segmentation technique for a wide range of applications. Deformable models, on the other hand, provide an explicit representation of the boundary and the ROI shape. They combine several desirable features such as inherent connectivity and smoothness, which counteract