Pablo Gautério Cavalcanti

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This paper describes a new method for classifying pigmented skin lesions as benign or malignant. The skin lesion images are acquired with standard cameras, and our method can be used in telemedicine by non-specialists. Each acquired image undergoes a sequence of processing steps, namely: (1) preprocessing, where shading effects are attenuated; (2)(More)
Several pigmented skin lesion segmentation methods have been proposed for dermoscopy images, however skin lesion segmentation on macroscopic images have not received much attention. Despite the fact that dermoscopy is a very specialized technique, in some practical situations, patients do not have a fast access to an specialist. In this situations,(More)
Segmentation is an important step in computer-aided diagnostic systems for pigmented skin lesions, since that a good definition of the lesion area and its boundary at the image is very important to distinguish benign from malignant cases. In this paper a new skin lesion segmentation method is proposed. This method uses Independent Component Analysis to(More)
In this paper, we propose a novel approach to discriminate malignant melanomas and benign atypical nevi, since both types of melanocytic skin lesions have very similar characteristics. Recent studies involving the non-invasive diagnosis of melanoma indicate that the concentrations of the two main classes of melanin present in the human skin, eumelanin and(More)
Melanoma is a type of malignant melanocytic skin lesion, and it is among the most life threatening existing cancers if not treated at an early stage. Computer-aided prescreening systems for melanocytic skin lesions is a recent trend to detect malignant melanocytic skin lesions in their early stages, and lesion segmentation is an important initial processing(More)
Melanoma is the most dangerous skin cancer. It should be diagnosed early because of its aggressiveness. To diagnose melanoma earlier, skin lesion should be segmented accurately. To reduce the cost for specialists to screen every patient, there is a need of automated melanoma prescreening system to diagnose melanoma using images acquired in digital cameras.(More)
This paper presents a new method to compute the head pose in monocular images by comparing the positions of specific facial features with the positions of these facial features in multiple instances of a prior 3D face model. Given an image containing a face, we locate facial features such as nose, eyes, and mouth. Then these 2D feature locations are used as(More)