Automatic identification of tuberculosis mycobacterium

• Autor(a) principal: Costa Filho,Cicero Ferreira Fernandes
• Data de Publicação: 2015
• Outros Autores: Levy,Pamela Campos, Xavier,Clahildek de Matos, Fujimoto,Luciana Botinelly Mendonça, Costa,Marly Guimarães Fernandes
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Research on Biomedical Engineering (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402015000100033
• topic: Tuberculosis; Automatic bacillus identification; Neural network; Support vector machine

Introduction According to the Global TB control report of 2013, “Tuberculosis (TB) remains a major global health problem. In 2012, an estimated 8.6 million people developed TB and 1.3 million died from the disease. Two main sputum smear microscopy techniques are used for TB diagnosis: Fluorescence microscopy and conventional microscopy. Fluorescence microscopy is a more expensive diagnostic method because of the high costs of the microscopy unit and its maintenance. Therefore, conventional microscopy is more appropriate for use in developing countries. Methods This paper presents a new method for detecting tuberculosis bacillus in conventional sputum smear microscopy. The method consists of two main steps, bacillus segmentation and post-processing. In the first step, the scalar selection technique was used to select input variables for the segmentation classifiers from four color spaces. Thirty features were used, including the subtractions of the color components of different color spaces. In the post-processing step, three filters were used to separate bacilli from artifact: a size filter, a geometric filter and a Rule-based filter that uses the components of the RGB color space. Results In bacillus identification, an overall sensitivity of 96.80% and an error rate of 3.38% were obtained. An image database with 120-sputum-smear microscopy slices of 12 patients with objects marked as bacillus, agglomerated bacillus and artifact was generated and is now available online. Conclusions The best results were obtained with a support vector machine in bacillus segmentation associated with the application of the three post-processing filters.