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Fernández, J.M.F., López, E.J.H., Llamas, F.S., Calvillo, A.R., Galeana, P.A.C., Pacheco, G.L., Palomar., M.G.G., Femat, R. and Velázquez, M.M. (2012) Development of an optimized multi-biomarker panel for the detection of lung cancer based on principal component analysis and artificial neural network modeling. Expert Systems with Applications, 39, 10851-10856.
http://dx.doi.org/10.1016/j.eswa.2012.03.008

has been cited by the following article:

• TITLE: 3D segmentation and visualization of lung and its structures using CT images of the thorax

  AUTHORS: Pedro P. Rebouças Filho, Paulo Cesar Cortez, Victor Hugo C. de Albuquerque

  KEYWORDS: 3D Region Growing; Lungs segmentation; COPD; Pulmonary Structure Visualization; Computed Tomography

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.6 No.11, November 25, 2013

  ABSTRACT: Computing systems have been playing an important role in various medical fields, notably in image diagnosis. Studies in the field of Computational Vision aim at developing techniques and systems capable of detecting various illnesses automatically. What has been highlighted among the existing exams that allow diagnosis aid and the application of computing systems in parallel is Computed Tomography (CT). CT enables the visualization of internal organs, such as the lung and its structures. Computational Vision systems extract information from the CT images by segmenting the regions of interest, and then recognize and identify details in those images. This work focuses on the segmentation phase of CT lung images with singularity-based techniques. Among these methods are the region growing (RG) technique and its 3D RG variations and the thresholding technique with multi-thresholding. The 3D RG method is applied to lung segmentation and from the 3D RG segments of the lung hilum, the multi-thresholding can segment the blood vessels, lung emphysema and the bones. The results of lung segmentation in this work were evaluated by two pulmonologists. The results obtained showed that these methods can integrate aid systems for medical diagnosis in the pulmonology field.