Robust pulmonary segmentation for chest radiography, combining enhancement, adaptive morphology and innovative active contours

• Autor(a) principal: Vital,Daniel Aparecido
• Data de Publicação: 2018
• Outros Autores: Sais,Barbara Teixeira, Moraes,Matheus Cardoso
• 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=S2446-47402018000300234
• topic: Lung segmentation; Chest radiographs; Discrete wavelet packet frame; Gradient vector flow; Binary morphology

Abstract Introduction Statistical data reveal that approximately 140 million radiological exams are performed annually in Brazil. These exams are designed to detect and to analyze fractures, caused by different types of trauma; as well as, to diagnose pathologies such as pulmonary diseases. For better visualization of those lesions or abnormalities, methods of image segmentation can be implemented. Such methods lead to the separation of the region of interest, which allows extracting the characteristics and anomalies of the desired tissue. However, the methods developed by researchers in this area still have restrictions. Consequently, we present an automatic pulmonary segmentation approach that overcomes these constraints. Methods This method is composed of a combination of Discrete Wavelet Packet Frame (DWPF), morphological operations and Gradient Vector Flow (GVF). The methodology is divided into four steps: Pre-processing - the original image is enhanced by discrete wavelet; Processing - where occurs a combination of the Otsu threshold with a series of morphological operations in order to identify the pulmonary object; Post-processing - an innovative form of using GVF improves the binary information of pulmonary tissue, and; Evaluation – the segmented images were evaluated for accuracy of detection the pulmonary region and border. Results The evaluation was carried out by segmenting 247 digital X-ray challenging images of the thorax human. The results show high for values of Overlap (97,63% ± 3.34%), and Average Contour Distance (0.69mm ± 0.95mm). Conclusion The results allow verifying that the proposed technique is robust and more accurate than other methods of lung segmentation, besides being a fully automatic method of lung segmentation.