title: RGB and HSV quantitative analysis of autofluorescence bronchoscopy used for characterization and identification of bronchopulmonary cancer.
abstract: Autofluorescence bronchoscopy (AFB) shows good sensitivity in detecting dysplasia and bronchopulmonary cancer. However, the poor specificity of AFB would lead to excessive biopsy. The aim of the study is to establish a more effective quantitative method (optimal identification index and reference value) for characterizing the AFB images within the region of interest and discuss AFB's significance in the diagnosis of central-type lung cancer. A total of 218 suspected lung cancer patients were enrolled in this study. A quantitative analysis based on color space (red, green, blue[RGB] and HSV system) was conducted and the result was compared with the final diagnosis obtained by the pathology of biopsy. Cases were divided into different groups according to the pathological diagnosis of normal bronchial mucosa, inflammation, low-grade preinvasive (LGD), high-grade preinvasive (HGD), and invasive cancer. Quantitative analyses in multi-color spaces for the lesions showed by AFB images were conducted by software MATLAB. Finally, there is statistical significance among the different groups in some parameter in RGB and HSV system. So, both RGB and HSV quantitative analysis of autofluorescence bronchoscopy are useful to define benign and malignant diseases, which can objectively guide the bronchoscopist in selecting sites for biopsy with good pathologic correlation.
authors.affiliation: Department of Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China.
authors.forename: Xiaoxuan
authors.initials: X
authors.lastname: Zheng
authors.affiliation: Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Hongkai
authors.initials: H
authors.lastname: Xiong
authors.affiliation: Department of Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of pulmonary medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China.
authors.forename: Baohui
authors.initials: B
authors.lastname: Han
authors.affiliation: Department of Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China.
authors.forename: Jiayuan
authors.initials: J
authors.lastname: Sun
mesh_terms: D001706:Biopsy; D001984:Bronchial Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D001999:Bronchoscopy / Q000379:methods; D005453:Fluorescence; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D060787:Neoplasm Grading; D061848:Optical Imaging / Q000379:methods; D012372:ROC Curve; D012189:Retrospective Studies
keywords: Autofluorescence bronchoscopy; bronchopulmonary cancer; diagnosis; medical image processing; white light bronchoscopy