Sharif Digital Repository

In order to plan precise treatment or accurate tumor removal surgery, brain tumor segmentation is critical for detecting all parts of tumor and its surrounding tissues. To visualize brain anatomy and detect its abnormalities, we use multi-modal Magnetic Resonance Imaging (MRI) as input. This paper introduces an efficient and automated algorithm based on the 3D bit-plane neighborhood concept for Brain Tumor segmentation using a rule-based learning algorithm. In the proposed approach, in addition to using intensity values in each slice, we consider sets of three consecutive slices to extract information from 3D neighborhood. We construct a Rule base using sequential covering algorithm. Through... 

In order to plan precise treatment or accurate tumor removal surgery, brain tumor segmentation is critical for detecting all parts of tumor and its surrounding tissues. To visualize brain anatomy and detect its abnormalities, we use multi-modal Magnetic Resonance Imaging (MRI) as input. This paper introduces an efficient and automated algorithm based on the 3D bit-plane neighborhood concept for Brain Tumor segmentation using a rule-based learning algorithm. In the proposed approach, in addition to using intensity values in each slice, we consider sets of three consecutive slices to extract information from 3D neighborhood. We construct a Rule base using sequential covering algorithm. Through... 

Magnetic resonance imaging (MRI) techniques provide detailed anatomic information non-invasively and without the use of ionizing radiation. The development of new pulse sequences in MRI has allowed obtaining images with high clinical importance and thus joint analysis (multispectral MRI) is required for interpretation of these images. Fuzz rule-based systems can combine many inputs from widely varying sources so that they can be useful for description of tissues in MRI. In a fuzzy system an error free and optimized classifier can be obtained by genetic algorithms. In this paper, we have utilized a genetic fuzzy system for modeling different tissues in brain MRI and proposed a statistical... 

Glioma is one of the most frequent primary brain tumors in adults that arise from glial cells. Automatic and accurate segmentation of glioma is critical for detecting all parts of tumor and its surrounding tissues in cancer detection and surgical planning. In this paper, we present a reliable classification framework for detection and segmentation of abnormal tissues including brain glioma tumor portions such as edemas and tumor core. This framework learns weighted features extracted from the 3D cubic neighborhoods regarding to gray-level differences that indicate the spatial relationships among voxels. In addition to intensity values in each slice, we consider sets of three consecutive... 

Glioma is a common type of tumor that develops in the brain. Due to many differences in the shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding tissues in cancer detection is a challenging task in cancer detection. In recent researches, the combination of atlas-based segmentation and machine learning methods have presented superior performance over other automatic brain MRI segmentation algorithms. To overcome the side effects of limited existing information on atlas-based segmentation, and the long training and the time consuming phase of learning methods, we proposed a semi-supervised learning framework by introducing a...