Sistema inteligente para o processamento de imagens digitais intrabucais oclusais

Several are the areas in which digital images are used in solving day-to-day problems. In medicine the use of computer systems have improved the diagnosis and medical interpretations. In dentistry it’s not different, increasingly procedures assisted by computers have support dentists in their tasks. Set in this context, an area of dentistry known as public oral health is responsible for diagnosis and oral health treatment of a population. To this end, oral visual inspections are held in order to obtain oral health status information of a given population. From this collection of information, also known as epidemiological survey, the dentist can plan and evaluate taken actions for the different problems identified. This procedure has limiting factors, such as a limited number of qualified professionals to perform these tasks, different diagnoses interpretations among other factors. Given this context came the ideia of using intelligent systems techniques in supporting carrying out these tasks. Thus, it was proposed in this paper the development of an intelligent system able to segment, count and classify teeth from occlusal intraoral digital photographic images. The proposed system makes combined use of machine learning techniques and digital image processing. We first carried out a color-based segmentation on regions of interest, teeth and non teeth, in the images through the use of Support Vector Machine. After identifying these regions were used techniques based on morphological operators such as erosion and transformed watershed for counting and detecting the boundaries of the teeth, respectively. With the border detection of teeth was possible to calculate the Fourier descriptors for their shape and the position descriptors. Then the teeth were classified according to their types through the use of the SVM from the method one-against-all used in multiclass problem. The multiclass classification problem has been approached in two different ways. In the first approach we have considered three class types: molar, premolar and non teeth, while the second approach were considered five class types: molar, premolar, canine, incisor and non teeth. The system presented a satisfactory performance in the segmenting, counting and classification of teeth present in the images.

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Neste trabalho é proposto o desenvolvimento de um sistema inteligente capaz de segmentar, contar e classificar individualmente dentes a partir de imagens fotográficas digitais intraorais oclusais.O sistema proposto faz uso combinado das técnicas de aprendizagem de máquina no caso a máquina de vetor de suporte e processamento digital de imagens. Primeiramente é feita uma segmentação baseada nas cores dos dentes e não dentes presentes na imagem através do uso de máquina de vetores de suporte. A partir da identificação das regiões de interesse, dentes e não dentes, os dados são representados de modo que a contagem, detecção de fronteiras e classificação dos dentes possa ser feita. Para contagem e detecção de fronteiras são utilizadas técnicas baseadas em operadores morfológicos, erosão e transformada watershed, respectivamente. A classificação quanto aos tipos de dentes é baseada na utilização dos descritores de posição e forma, sendo esse último definido por descritores de Fourier. O sistema portanto é capaz de realizar a segmentação, a contagem e a classificação de dentes presentes nas imagens.