Segmentação de Fissura 3D em imagens de TC baseadas em Texturas
(3D Lung Fissure Segmentation in TC images based in Textures)
Edson Cavalcanti Neto (email@example.com)1, Paulo César Cortez (firstname.lastname@example.org)1, Tarique Silveira Cavalcante (email@example.com)1, Valberto Enoc Rodrigues (firstname.lastname@example.org)2, Pedro Pedrosa Rebouças Filho (email@example.com)2, Marcelo Alcântara Holanda (firstname.lastname@example.org)1
1Federal University of Ceará2Federal Institute of Science and Technology
This paper appears in: Revista IEEE América Latina
Publication Date: Jan. 2016
Volume: 14, Issue: 1
Among all cancers, lung cancer (LC) is the most common of all malignant tumors. In order to obtain a more effective segmentation of pulmonary fissures, independent to other structures present in the CT scan, this paper proposes the segmentation of 3D fissures using texture measures and Artificial Neural Networks (ANN). The results of this study are based on voxels classified as fissure through the proposed method compared to the gold standard set by an expert. The results were analyzed using similarity coefficient rate of 95.6%, the rate sensitivity of 71.1% and specificity of 95.6% rate. Thus, it is possible to identify the job has a gain due to not using segmentation of other pulmonary structures and does not require the use of pulmonary atlas.
Fissure, Neural Network, Texture, Image Processing
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