Support System for Semiautomatic Quantification of Pulmonary Fibrosis in CT Images | Zendy

Diomar Enrique Rodríguez-Obregón | Zendy

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Support System for Semiautomatic Quantification of Pulmonary Fibrosis in CT Images

Author(s) -

Diomar Enrique Rodríguez-Obregón

Publication year - 2017

Language(s) - English

Resource type - Dissertations/theses

DOI - 10.17488/rmib.381.11

Subject(s) - artificial intelligence , clinical practice , segmentation , fibrosis , field (mathematics) , medicine , computed tomography , computer vision , computer science , pulmonary disease , pulmonary fibrosis , pattern recognition (psychology) , radiology , mathematics , pathology , family medicine , pure mathematics

A method to estimate the pulmonary fibrosis in computed tomography (CT) imaging is presented. A semi-automatic segmentation algorithm based on the Chan-Vese method was used. The proposed method shows a similar fibrosis region with respect to clinical expert. However, the results need to be validated in a bigger data base. The proposed method approximates a fibrosis percentage that allows to achieve this procedure easily in order to support its implementation in the clinical practice minimizing the clinical expert subjectivity and generating a quantitativeestimation of fibrosis region.

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