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The abnormality of brain tissue always becomes a crucial issue in medical field. This medical condition can be recognized through segmentation of certain region from medical images obtained from MRI dataset. Image processing is one of computational methods which very helpful to analyze the MRI data. In this study, combination of segmentation and rendering image were used to isolate tumor and stroke. Two methods of thresholding were employed to segment the abnormality occurrence, followed by filtering to reduce non-abnormality area. Each MRI image is labeled and then used for volume estimations of tumor and stroke-attacked area. The algorithms are shown to be successful in isolating tumor and stroke in MRI images, based on thresholding parameter and stated detection accuracy. [ABSTRACT FROM AUTHOR]

We present a method for computing surface models from MRI images with specific design for vocal tract extraction. As the vocal tract consists of empty space with connection to the outside, it is difficult to segment with common methods based on consideration of intensity differences. In the proposed method, surfaces are extracted via three steps. First, void voxels in an image of a human head are extracted using a morphological closing operator. Then, the significant and unnecessary noise contained in the results is removed via connected-component labeling. Finally, a vocal tract mesh is formed by applying the Marching Cubes algorithm in the extracted region and used to provide masked images. As the method does not require user input, it is efficient for batch processing. [ABSTRACT FROM PUBLISHER]

Region growing is an important application of image segmentation in medical research for detection of tumour. In this paper, we propose an effective modified region growing technique for detection of brain tumour. Modified region growing includes an orientation constraint in addition to the normal intensity constrain. The performance of the proposed technique is systematically evaluated using the MRT brain images received from the public sources. For validating the effectiveness of the modified region growing, the quantity rate parameter has been considered. For the evaluation of the proposed technique of tumor detection, the sensitivity, specificity and accuracy values were used. Comparative analyses were made for the normal and the modified region growing using both the Feed Forward Neural Network (FFNN) and Radial Basis Function (RBF) neural network. The results show that the modified region growing achieved better results when compared to the normal technique. [ABSTRACT FROM PUBLISHER]