The automatic diagnosis of melanoma is usually affected by the noise that is often included in an image, during the acquisition stage or by superficial factors such as hair. Specifically, hair on the surface of a lesion can cause enough distortion, resulting in an erroneous diagnosis of the region of interest. To solve this issue, several techniques have been presented to detect hair on the surface of a dermoscopy image and substitute a surface approximation for these regions. Nonetheless, the existing methods are prone to false detections or reconstructions that are not uniform, demand high computing resources and modify the textures of important characteristics. Therefore, we proposed a method that detects the hairs by means of a convolution of the image with a kernel belonging to the first derivative of the Gaussian function and replaces the hairs using a multiscale morphological reconstruction. In addition, we integrated a refining stage that contributes to maintaining the quality of the patterns on the lesion. We used 36 dermoscopy images in the evaluation, which included a total of 586 hairs that were automatically detected with the proposed process and validated with their respective manual segmentations. Our results showed sensitivity and specificity performance measurements of 94.14% and 99.89%, respective.

In this work is proposed a new fully automated methodology using computer vision and dynamic programming to obtain a 3D reconstruction model of surfaces using scanning electron microscope (SEM) images based on stereovision. The horizontal stereo matching step is done with a robust and efficient algorithm based on semi-global matching. The cost function used in this study is very simple since the brightness and contrast change of corresponding pixels is negligible for the small tilt involved in stereo SEM. It is used a sum of absolute differences (SAD) over a variable pixel size window. Since it relies on dynamic programming, the matching algorithm uses an occlusion parameter which penalizes large depth discontinuities and, in practice, smooths the disparity map and the corresponding reconstructed surface. This step yields a disparity map, i.e. the differences between the horizontal coordinates of the matching points in the stereo images. The horizontal disparity map is finally converted into heights according to the SEM acquisition parameters: tilt angle, image magnification and pixel size. A validation test was first performed using as reference a microscopic grid with manufacturer specifications. Finally, with the 3D model are proposed some applications in materials science as roughness parameters estimation and wear measurements.