The detection and visualization of brain tumors on T2-weighted MRI images using multiparameter feature blocks

The objective of this paper is to present an analytical method to detect lesions or tumors in digitized medical images for 3D visualization. The authors developed a tumor detection method using three parameters; edge (E), gray (G), and contrast (H) values. The method proposed here studied the EGH parameters in a supervised block of input images. These feature blocks were compared with standardized parameters (derived from normal template block) to detect abnormal occurrences, e.g. image block which contain lesions or tumor cells. The abnormal blocks were transformed into three-dimension space for visualization and studies of robustness. Experiments were performed on different brain disease based on single and multiple slices of the MRI dataset. The experiments results have illustrated that our proposed conceptually simple technique is able to effectively detect tumor blocks while being computationally efficient. In this paper, we present a prototype system to evaluate the performance of the proposed methods, comparing detection accuracy and robustness with 3D visualization