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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 earlydetection, colposcopic images of the cervix are used for searching for possibleinjuries or abnormalities. An inherent characteristic of these images is thepresence of specular reflections (brightness) that make it difficult to observesome regions, which might imply misdiagnosis. In this paper, a new strategybased on neural networks is introduced for eliminating specular reflections andestimating the unobserved anatomical cervix portion under the bright zones. Forovercoming the fact that the ground truth corresponding to the specularreflection regions is always unknown, the new strategy proposes the supervisedtraining of a neural network to learn how to restore any hidden regions ofcolposcopic images. Once the specular reflections are identified, they areremoved from the image, and the previously trained network is used to fulfillthese deleted areas. The quality of the processed images was evaluatedquantitatively and qualitatively. In 21 of the 22 evaluated images, thedetected specular reflections were eliminated, whereas, in the remaining one,these reflections were almost completely eliminated. The distribution of thecolors and the content of the restored images are similar to those of theoriginals. The evaluation carried out by a specialist in Cervix Pathologyconcluded that, after eliminating the specular reflections, the anatomical andphysiological elements of the cervix are observable in the restored images,which facilitates the medical diagnosis of cervical pathologies. Our method hasthe potential to improve the early detection of cervical cancer.

Specular reflections removal in colposcopic images based on neural networks: Supervised training with no ground truth previous knowledge