MRI Segmentation assumes great importance research and clinical applications. There are many methods that exist to segment the brain. Of these, conventional methods that use pure image processing techniques are not preferred because they need human interaction for accurate and reliable segmentation. Unsupervised methods, on the other hand, do not require any human interference and can segment the brain with high precision. For this reason, unsupervised methods are preferred over conventional methods. Many unsupervised methods such as Fuzzy c-means, Finite Gaussian Mixture Model, Artificial Neural Networks, etc. exist. In this project, we consider two such unsupervised methods based on FCM and FGMM. Results show that FCM performs better in terms of computational complexity and accuracy, while FGMM has advantages such as learning the number of classes automatically.
Many signal processing algorithms can be accelerated using reconfigurable hardware. To achieve a good speedup compared to running software on a general purpose processor, fine-grained control over the bitwidth of each component in the datapath is desired. This goal can be achieved by using NU’s variable precision floating-point library. To analyze the usefulness of the floating-point divide unit, we incorporate it into our previous implementation of the Kmeans clustering algorithm applied to multispectral satellite images. With the lack of a floating-point divide hardware implementation, the mean updating step in each iteration of the K-means algorithm had to be moved to the host computer for calculation. The new means calculated on the host then had to be moved back to the FPGA board for the next iteration of the algorithm. This added data transfer overhead between the host and the FPGA board.
Abstract—We introduce a family of first-order multidimensional ordinary differential equations (ODE’s) with discontinuous right-hand sides and demonstrate their applicability in image processing. An equation belonging to this family is an inverse diffusion everywhere except at local extrema, where some stabilization is introduced. For this reason, we call these equations “stabilized inverse diffusion equations” (SIDE’s). Existence and uniqueness of solutions, as well as stability, are proven for SIDE’s. A SIDE in one spatial dimension may be interpreted as a limiting case of a semi-discretized Perona–Malik equation , . In an experimental section, SIDE’s are shown to suppress noise while sharpening edges present in the input signal. Their application to image segmentation is also demonstrated.