Segmentação de bacilos de tuberculose em imagens de microscopia convencional através da utilização de redes neurais artificiais e máquinas de vetores suporte - Soares, Lucas de Assis (2015)
Even though tuberculosis (TB) is a treatable disease, it is still a major global health problem being second only to AIDS as the greatest killer worldwide due to a single infectious agent (WHO, 2015). In order to treat it, the disease must be properly diagnosed. The diagnosis is usually done using by staining a slide with patient sputum using the Ziehl-Neelsen stain and then a human specialist analyzes it using an optical microscope looking for tuberculosis bacilli. Since this process is time consuming and labor intensive, an automatic bacilli recognition system allows the diagnosis process to be more agile and less tiresome. In this work, an automatic tuberculosis bacilli segmentation system using conventional microscopy images is proposed. The system is basically divided in two stages: a stage of segmentation and a stage for classification of the segmented structures. First, images are projected based on a linear discriminant analysis considering Fisher criterion in order to increase the separation between bacilli pixels and background pixels. Then, two approaches are evaluated: a segmentation process based on global thresholding and another based on Otsu segmentation method. Structures that have a big or a small area are then filtered and morphological operators are applied over the binary image. Finally, the segmented structures are classified using artificial neural networks and support vector machines. The results show that it is possible to implement an automatic tuberculosis bacilli segmentation system that provides a good distinction of bacilli in the images with a low computational cost. For the segmentation stage, up to 98.69% of bacilli are correctly segmented and up to 85.51% of bacilli remain after the area filter. For the classification of the structures, mean values up to 94.25%, 95.33%, 95.73% and 92.50% were obtained for sensitivity, specificity, accuracy and precision, respectively.