In the recent health care, medical imaging plays a significant role throughout the entire clinical process from diagnostics and treatment planning to surgical procedures and follow up studies. During the acquisition of medical images, medical images in the most imaging modalities typically suffer from one or more of the following challenges regarding (i) low resolution (in the spatial and spectral domains), (ii) high level of noise, (iii) low contrast, (iv) geometric deformations, and (v) presence of imaging artifacts. Low resolution and low contrast imperfections can be avoided by finer spatial sampling, which may be obtained through a longer acquisition time. Nevertheless, that would also increase the probability of geometric transformations, e.g. patient movement, and thus cause blurring in the image. The imaging artifacts also end up the challenging problems in the analysis of medical images. The first problem is the size of the medical image datasets. Due to the large datasets of medical images, image processing and visualization algorithms have to be adjusted with advanced parallelization techniques using supercomputers with graphical processing units. The second problem is segmentation. Segmentation is the problem of extracting anatomical structures for quantitative shape analysis or visualization. Segmentation should be fast, easy to use, robust with regards to image artifacts, and as automatic as possible in the ideal clinical application. The ultimate goal of segmentation is to create structured visual representation from an unstructured raw data. Final problem is registration, which aims fusing images of the same region acquired from different modalities (e.g. MRI and CT) or putting in correspondence images of one patient at different times or of different patients. In surgery, for example, images are acquired before (pre-operative), as well as during (intra-operative) surgery. Due to time constraints, the real-time intraoperative images have a lower resolution than the pre-operative images obtained before surgery. Moreover, geometric deformations which occur naturally during surgery make it difficult to relate the high-resolution pre-operative image to the lower-resolution intra-operative anatomy of the patient. By the help of image registration, surgeons are able to relate the two sets of images. In conclusion, medical image analysis remains a vital field of research. None of the problem areas above are satisfactorily solved and the problems are still open to improvement.