This work introduces a new brain tumor segmentation method based on a new criterion function for threshold selection. The method aims to accomplish some features that are desirable in medical practice such as simplicity, speed, accuracy, and independency of user inputs or parameters. Although the criterion function can be seen as an extension of Otsu's criterion, we did not assume low intra-variance for both classes, object and background. The new criterion is adapted to treat the tumor as the object to be segmented and a portion of healthy brain tissue as the background. In order to speed up the search for the thresholds, the segmentation method exploits domain knowledge by using some feature points in the histogram to bound a range of interest for searching. We evaluated the method on a set of 30 patient cases with their respective magnetic resonance images and compared the criterion function against other well-known criteria for threshold selection. The new criterion outperformed the other criteria in segmenting the complete region affected by the tumor for each patient case.

Enhancement of the quality of a digital image is desirable in several scenarios such as underwater-image analysis, where improving visibility is necessary to reduce alterations caused by unbalanced lighting and the presence of sediments, among others. Many algorithms have been proposed oriented towards treatment of specific factors, such as contrast, color fidelity, noise, and lighting. This work explores the literature techniques and establishes a filter sequence for enhancing underwater images based on a pre-established quality metric. The resulting sequence begins by balancing the lighting using homomorphic filtering, improve the contrast by the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, and finally noise reduction and edge enhancement are done by using bilateral filtering. The results of the implementation suggest a qualitative improvement, in contrast, color, and sharpness of borders.