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Qurat-Ul-Ain, Ghazanfar Latif, Sidra Batool Kazmi, and Arfan Jaffar, M.(2010) Classification and Segmentation of Brain Tumor using Texture Analysis., Proceedings of the 9th WSEAS International Conference on Recent Advances In Artificial Intelligence, Knowledge Engineering and Data Bases , February 20-22, 2010 ,147-155.

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• TITLE: A Semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI

  AUTHORS: S. Ananda Resmi, Tessamma Thomas

  KEYWORDS: 3D Modeling; Glioma Tumor; Segmentation; Volumetric Analysis; Brain MRI

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.5 No.7, July 3, 2012

  ABSTRACT: This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the preoperative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These segmented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming process and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radiologist.