Breast cancer is considered as the second leading cause of cancer deaths among women in the United States. Early detection of cancer is crucial in order to reduce its negative effects. Recently, magnetic resonance imaging (MRI) has become an important modality in the detection of breast cancer in daily practice. However, routine breast MRI has a moderate specificity that may increase its false positive rates. Therefore, automated detection techniques of malignancy can provide an important tool for clinicians. In this study, different data classification methods were examined to classify breast tumors screened using contrast enhanced MRI. The used data set included 20 subjects categorized clinically into two groups; benign and malignant tumors. MRI scans were first preprocessed to extract imaging features. Then two classification methods were exploited to differentiate between the two tumor's categories using the extracted features. The used classification methods were K-Nearest Neighbor (KNN), and Linear Discriminant Analysis (LDA). The results show a relatively significant classification accuracy compared with pathological analysis, and also the calculated resubstitution error. In summary, the proposed automatic classification techniques can be used as noninvasive diagnostic tools for breast cancer, with the capability of decreasing false positive errors associated with regular MRI diagnosis

In this paper, a new approach for segmenting different anatomical regions in dental Computed Tomography (CT) studies is presented. The approach consists of three steps: Hounsfield unit's threshold (HU) based on gray-level segmentation, multi-object with texture extraction and anatomical regions identification. First, a HU threshold window sets to separate between different regions upon their gray-level values; second, a set of objects are generated by and texture descriptors are calculated for selected windows from the image data sample. Finally, identification of different anatomical regions set for mandible bones cortical and cancellous. It is expected that the proposed approach will also help automate different semi-automatic segmentation techniques by providing initial boundary points for deformable models or seed points for split and merge segmentation algorithms. Preliminary results obtained for dental CT studies of human-mandible are presented.

A new approach for segmenting different anatomical regions in dental Computed Tomography (CT) studies is presented in this paper. It is expected that the proposed approach will help automate different tissues regions by providing initial boundary points for deformable models or seed points for split and merge segmentation algorithms. Preliminary results obtained for dental CT studies of dentate and edentulous human-mandible are presented with finite element models divided by tetrahedral elements, which built based on actual CT data combined with triangulation equations. Identification of different anatomical regions set for mandible cortical and cancellous bones by generated 3D models by marching cubes technique.

In this paper, a new approach for computing different anatomical regions in dental Computed Tomography (CT) is presented. The approach consists of two steps. First, a HU threshold window sets to separate between different regions upon their gray-level values; second, a set of objects are generated and texture descriptors are calculated for selected windows from the image data sample. Finally, identification of different anatomical regions set for mandible bones to determine the cystic lesion volume based on numerical methods. Preliminary results obtained for dental CT of female patient aged 14 years old complaining of bilateral swelling in her mandible are presented.