Quantization Methodology of Autofluorescence Bronchoscopy Image in the YUV System
Background and objective The aim of this study is to determine the best reference values of the optimal evaluation indexes that identify different disease types. Disease identification was conducted using the YUV quantitative analysis of autofluorescence bronchoscopy (AFB) images in the target areas...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Chinese Anti-Cancer Association; Chinese Antituberculosis Association
2014-11-01
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| Series: | Chinese Journal of Lung Cancer |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.3779/j.issn.1009-3419.2014.11.05 |
| _version_ | 1818387178913267712 |
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| author | Xiaoxuan ZHENG Hongkai XIONG Yong LI Baohui HAN Jiayuan SUN |
| author_facet | Xiaoxuan ZHENG Hongkai XIONG Yong LI Baohui HAN Jiayuan SUN |
| author_sort | Xiaoxuan ZHENG |
| collection | DOAJ |
| description | Background and objective The aim of this study is to determine the best reference values of the optimal evaluation indexes that identify different disease types. Disease identification was conducted using the YUV quantitative analysis of autofluorescence bronchoscopy (AFB) images in the target areas. Furthermore, this study discusses the significance of AFB in the diagnosis of the central-type lung cancer. Methods A biopsy was conducted for cases that showed pathologic changes under either autofluorescence or white-light bronchoscopy. Moreover, MATLAB was used to carry out the quantitative analyses of lesion in multi-color spaces from AFB images. The cases were divided into different groups according to the pathological diagnosis of normal bronchial mucosa, inflammation, low-grade dysplasia (LGD), high-grade dysplasia (HGD), and invasive cancer. SPSS 11.5 was used to process the data for statistical analysis. Results The Y values were different and statistically different between invasive cancer and LGD (P<0.001) and invasive cancer and inflammation (P=0.040), respectively. The U values between invasive cancer and the other groups were statistically different (P<0.050). Similarly, the V values between invasive cancer and LGD and inflammation and normal bronchial mucosa were different. Lastly, the V values between normal bronchial mucosa and HGD and inflammation and normal bronchial mucosa were different. Conclusion The YUV values in the AFB effectively identified benign and malignant diseases and were proven to be effective scientific bases for the accurate AFB diagnosis of lung cancer. |
| first_indexed | 2024-12-14T04:05:49Z |
| format | Article |
| id | doaj.art-51ef074d344d4c958791520779f8401b |
| institution | Directory Open Access Journal |
| issn | 1009-3419 |
| language | zho |
| last_indexed | 2024-12-14T04:05:49Z |
| publishDate | 2014-11-01 |
| publisher | Chinese Anti-Cancer Association; Chinese Antituberculosis Association |
| record_format | Article |
| series | Chinese Journal of Lung Cancer |
| spelling | doaj.art-51ef074d344d4c958791520779f8401b2022-12-21T23:17:49ZzhoChinese Anti-Cancer Association; Chinese Antituberculosis AssociationChinese Journal of Lung Cancer1009-34192014-11-01171179780310.3779/j.issn.1009-3419.2014.11.05Quantization Methodology of Autofluorescence Bronchoscopy Image
in the YUV SystemXiaoxuan ZHENG0Hongkai XIONG1Yong LI2Baohui HAN3Jiayuan SUN4Department of Endoscopy Room, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, ChinaDepartment of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaDepartment of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaDepartment of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, ChinaDepartment of Endoscopy Room, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, ChinaBackground and objective The aim of this study is to determine the best reference values of the optimal evaluation indexes that identify different disease types. Disease identification was conducted using the YUV quantitative analysis of autofluorescence bronchoscopy (AFB) images in the target areas. Furthermore, this study discusses the significance of AFB in the diagnosis of the central-type lung cancer. Methods A biopsy was conducted for cases that showed pathologic changes under either autofluorescence or white-light bronchoscopy. Moreover, MATLAB was used to carry out the quantitative analyses of lesion in multi-color spaces from AFB images. The cases were divided into different groups according to the pathological diagnosis of normal bronchial mucosa, inflammation, low-grade dysplasia (LGD), high-grade dysplasia (HGD), and invasive cancer. SPSS 11.5 was used to process the data for statistical analysis. Results The Y values were different and statistically different between invasive cancer and LGD (P<0.001) and invasive cancer and inflammation (P=0.040), respectively. The U values between invasive cancer and the other groups were statistically different (P<0.050). Similarly, the V values between invasive cancer and LGD and inflammation and normal bronchial mucosa were different. Lastly, the V values between normal bronchial mucosa and HGD and inflammation and normal bronchial mucosa were different. Conclusion The YUV values in the AFB effectively identified benign and malignant diseases and were proven to be effective scientific bases for the accurate AFB diagnosis of lung cancer.http://dx.doi.org/10.3779/j.issn.1009-3419.2014.11.05Lung neoplasmsDiagnosisAutofluorescence bronchoscopyWhite-light bronchoscopyMedical image processing |
| spellingShingle | Xiaoxuan ZHENG Hongkai XIONG Yong LI Baohui HAN Jiayuan SUN Quantization Methodology of Autofluorescence Bronchoscopy Image in the YUV System Chinese Journal of Lung Cancer Lung neoplasms Diagnosis Autofluorescence bronchoscopy White-light bronchoscopy Medical image processing |
| title | Quantization Methodology of Autofluorescence Bronchoscopy Image
in the YUV System |
| title_full | Quantization Methodology of Autofluorescence Bronchoscopy Image
in the YUV System |
| title_fullStr | Quantization Methodology of Autofluorescence Bronchoscopy Image
in the YUV System |
| title_full_unstemmed | Quantization Methodology of Autofluorescence Bronchoscopy Image
in the YUV System |
| title_short | Quantization Methodology of Autofluorescence Bronchoscopy Image
in the YUV System |
| title_sort | quantization methodology of autofluorescence bronchoscopy image
in the yuv system |
| topic | Lung neoplasms Diagnosis Autofluorescence bronchoscopy White-light bronchoscopy Medical image processing |
| url | http://dx.doi.org/10.3779/j.issn.1009-3419.2014.11.05 |
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