  Cervical cancer is a malignant tumor that seriously threatens women's health,
and is one of the most common that affects women worldwide. For its early
detection, colposcopic images of the cervix are used for searching for possible
injuries or abnormalities. An inherent characteristic of these images is the
presence of specular reflections (brightness) that make it difficult to observe
some regions, which might imply misdiagnosis. In this paper, a new strategy
based on neural networks is introduced for eliminating specular reflections and
estimating the unobserved anatomical cervix portion under the bright zones. For
overcoming the fact that the ground truth corresponding to the specular
reflection regions is always unknown, the new strategy proposes the supervised
training of a neural network to learn how to restore any hidden regions of
colposcopic images. Once the specular reflections are identified, they are
removed from the image, and the previously trained network is used to fulfill
these deleted areas. The quality of the processed images was evaluated
quantitatively and qualitatively. In 21 of the 22 evaluated images, the
detected specular reflections were eliminated, whereas, in the remaining one,
these reflections were almost completely eliminated. The distribution of the
colors and the content of the restored images are similar to those of the
originals. The evaluation carried out by a specialist in Cervix Pathology
concluded that, after eliminating the specular reflections, the anatomical and
physiological elements of the cervix are observable in the restored images,
which facilitates the medical diagnosis of cervical pathologies. Our method has
the potential to improve the early detection of cervical cancer.
