Breast thermography uses ultrasensitive infrared cameras to produce high resolution images of temperatures and vascular changes. In the present work we propose the development of computational methods that allow the analysis and digital processing of breast thermographic images for the detection of regions related to a probable lesion. A semi-automated segmentation algorithm is presented by means of polynomial curve Ötting for the detection a region of breast (ROI). A public database which contains information on volunteers with diagnose made by mammography and biopsy is used.
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