Brain tumor segmentation from MRI data sets using region growing approach

Brain segmentation is an important part of medical image processing. Most commonly, it aims at locating various lesions and pathologies inside the human brain. In this paper, a new brain segmentation algorithm is proposed. The method is seeded region growing approach which was developed to segment the area of the brain affected by a tumor. The proposed algorithm was described. Results of testing the developed method on real MRI data set are presented and discussed.

[1]  Borut Marincek,et al.  How Does MRI Work? An Introduction to the Physics and Function of Magnetic Resonance Imaging , 2007, Journal of Nuclear Medicine.

[2]  Annette Sterr,et al.  MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization , 2005, IEEE Transactions on Information Technology in Biomedicine.

[3]  R. Kikinis,et al.  Automated segmentation of MR images of brain tumors. , 2001, Radiology.

[4]  Lawrence O. Hall,et al.  Automatic tumor segmentation using knowledge-based techniques , 1998, IEEE Transactions on Medical Imaging.

[5]  Guido Gerig,et al.  A brain tumor segmentation framework based on outlier detection , 2004, Medical Image Anal..

[6]  Max A. Viergever,et al.  Automatic Morphology-Based Brain Segmentation (MBRASE) from MRI-T1 Data , 2000, NeuroImage.

[7]  M. Stella Atkins,et al.  Fully automatic segmentation of the brain in MRI , 1998, IEEE Transactions on Medical Imaging.

[8]  Rainer Goebel,et al.  An Efficient Algorithm for Topologically Correct Segmentation of the Cortical Sheet in Anatomical MR Volumes , 2001, NeuroImage.

[9]  Verónica Médina-Bañuelos,et al.  Data-driven brain MRI segmentation supported on edge confidence and a priori tissue information , 2006, IEEE Transactions on Medical Imaging.

[10]  Daoqiang Zhang,et al.  Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  R. Leahy,et al.  Magnetic Resonance Image Tissue Classification Using a Partial Volume Model , 2001, NeuroImage.