Background and Objective. Skin cancer is the most common cancer worldwide. One of the most common non-melanoma tumors is basal cell carcinoma (BCC), which accounts for 75% of all skin cancers. There are many benign lesions that can be confused with these types of cancers, leading to unnecessary biopsies. In this paper, a new method to identify the different BCC dermoscopic patterns present in a skin lesion is presented. In addition, this information is applied to classify skin lesions into BCC and non-BCC. Methods. The proposed method combines the information provided by the original dermoscopic image, introduced in a convolutional neural network (CNN), with deep and handcrafted features extracted from color and texture analysis of the image. This color analysis is performed by transforming the image into a uniform color space and into a color appearance model. To demonstrate the validity of the method, a comparison between the classification obtained employing exclusively a CNN with the original image as input and the classification with additional color and texture features is presented. Furthermore, an exhaustive comparison of classification employing different color and texture measures derived from different color spaces is presented. Results. Results show that the classifier with additional color and texture features outperforms a CNN whose input is only the original image. Another important achievement is that a new color cooccurrence matrix, proposed in this paper, improves the results obtained with other texture measures. Finally, sensitivity of 0.99, specificity of 0.94 and accuracy of 0.97 are achieved when lesions are classified into BCC or non-BCC. Conclusions. To the best of our knowledge, this is the first time that a methodology to detect all the possible patterns that can be present in a BCC lesion is proposed. This detection leads to a clinically explainable classification into BCC and non-BCC lesions. In this sense, the classification of the proposed tool is based on the detection of the dermoscopic features that dermatologists employ for their diagnosis.

Color has great diagnostic significance in dermatoscopy. Several diagnosis methods are based on the colors detected within a lesion. Malignant lesions frequently show more than three colors, whereas in benign lesions, three or fewer colors are usually observed. Black, red, white and blue-gray are found more frequently in melanomas than in benign nevi. In this paper, a method to identify the colors of a lesion automatically is presented. A color label identification problem is proposed and solved by maximizing the posterior probability of a pixel to belong to a label, given its color value and the neighborhood color values. The main contribution of this work is the estimation of the different terms involved in the computation of this probability. Two evaluations are performed on a database of 200 dermoscopic images. The first one evaluates if all the colors detected in a lesion are indeed present in it. The second analyzes if each pixel within a lesion is assigned the correct color label. The results show that the proposed method performs correctly and outperforms other methods, with an average F-measure of 0.89, an accuracy of 0.90 and a Spearman correlation of 0.831.

<p><strong>Abstract.</strong> Biophysical conditions, the lack of governance and the weak applications of planning and territorial planning policies are aggravating factors of affections caused by natural disasters in Latin America. In Ecuador, landslides have caused not only material but also human losses, which shows the lack of immediate actions to avoid human settlements in risk areas or more controlled evacuations protocols, in case of early detection of risk areas due to this type of events. This article is part of an applied research project for the City of Cuenca allowing to take advantage of the information provided by citizens with a PPGIS. Furthermore, remote sensor images are analyzed, in order to identify in a semi-automated way, risk areas due to mass movements problems. Two approaches for semi-automated detecting areas where mass movements happen have been explored, i) Temporal images obtained by a LiDAR; and ii) Temporary images obtained by flights of an unmanned aerial vehicle, UAV. This paper present the procedure followed by comparison of image outcomes. The resulting analysis clearly presents the applicability of the two techniques thus allowing the determination of maps and early detection of landslides.</p>