Melanoma is the most deadly form of skin cancer in human in all over the world with an increase number of victims yearly. One traditional form of diagnosis melanoma is by using the so called ABCDE rule which stands for Asymmetry, Border, Color, Diameter and Evolution of the lesion. For melanoma lesions, the color as a descriptor exhibits heterogeneous values, ranging from light brown to dark brown (sometimes blue reddish or even white). Therefore, investigating on color features from digital melanoma images could provide insights for developing automated algorithms for melanoma discrimination from common nevus. In this research work, an algorithm is proposed and tested to characterize the color in a pigmented lesion. The developed algorithm measures the hue of different sites in the same pigmented area from a digital image using the HSI color space. The algorithm was applied to 40 digital images of unequivocal melanomas and 40 images of common nevus, which were taken from several data bases. Preliminary results indicate that visible color changes of melanoma sites are well accounted by the proposed algorithm. Other factors, such as quality of images and the influence of the shiny areas on the results obtained with the proposed algorithm are discussed.
Autofocus is of fundamental importance for a real time automatic system. In many microscopy applications, a
desired automatic system should provide the best focused image with enough accuracy and the least computation
time. During the last years, several metrics based on images have been proposed for the autofocus process.
Although many of these techniques present good results, their main limitations reside in the high computation
time. Recently, the development of graphics processing units (GPUs) has given place to new scientific applications
oriented to diminish the computational effort of the central processing unit (CPU). This manuscript presents the
parallel implementation of eight different autofocus algorithms using GPUs for microscopy applications. The main
objective of the proposed manuscript is to demonstrate that the use of GPUs can speed up the computational
time required to perform the mentioned techniques. The reduction of computation time achieved with the
proposed implementation suggests that graphics processing units can effectively be used for autofocus in real