Using Local Normalization and Local Thresholding in the Detection of Small Objects in MR Brain Images

A lot of time and effort has been put into finding an automatic segmentation system which could detect the region and position of white matter hypersensitivities in MRI brain scans. At the cellular level, changes in the white matter of the brain can be understood as a loss of myelin around axons. These changes might be detected by MRI due to local changes in water content. By establishing a method which could detect such irregularities, it would be possible to help diagnose some serious autoimmune diseases. In this work, a wide variety of local thresholding algorithms that are applied after local normalization is evaluated in terms of how accurately they segment these hypersensitivities. With the use of ImageJ, the following algorithms were tested: Bernsen, Contrast, Mean, Median, MidGrey, Otsu, Sauvola and SDA. The most accurate segmentation results are presented for all algorithms. For most of the ALT algorithms, the use of local normalization increased the achieved Dice value by as much as 100%. Contrary to what might be expected, the best results were achieved for various values of the sigma1 and sigma2 parameters of local normalization, both for single images and for a group of algorithms for a given image, thus it is difficult to apply the presented solutions to other examples.