Advanced Image Analysis Tools Development for the Early Stage Bronchial Cancer Detection
Autofluorescence (AF) bronchoscopy is an
established method to detect dysplasia and carcinoma in situ (CIS).
For this reason the “Sotiria" Hospital uses the Karl Storz D-light
system. However, in early tumor stages the visualization is not that
obvious. With the help of a PC, we analyzed the color images we
captured by developing certain tools in Matlab®. We used statistical
methods based on texture analysis, signal processing methods based
on Gabor models and conversion algorithms between devicedependent
color spaces. Our belief is that we reduced the error made
by the naked eye. The tools we implemented improve the quality of
patients' life.
[1] H. van den Bergh, "Early Detection of Lung Cancer and the Role of
Endoscopic Fluorescence Imaging", Med. Laser Appl. 18: 20-26, pp 20-
26, 2003.
[2] E. Passalidou, "Early Detection of the Lung Cancer", Pneumon vol. 3,
issue 15, Sept-Dec 2002.
[3] N. Apostolou, M. Haritou, N. Lolis, D. Beldekis, A. Rasidakis, D.
Koutsouris, "Advanced Platform for Lung Cancer Detection in
Autofluorescence Bronchoscopy Images. Clinical Application at the
"Sotiria" Hospital, Hellas", J. Qual. Life Res., pg. 154-160, vol. 3, issue
2, 2005.
[4] R. C. Gonzalez, R. E. Woods, S. L. Eddins, Digital Image Processing
Using Matlab. New Jersey: Ed. Pearson Prentice Hall, 2004.
[5] R. C. Gonzalez, R. E. Woods, Digital Image Processing, Addison-
Wesley Publ. Co., 1993.
[6] M. Tuceryan, A. K. Jain, The Handbook of Pattern Recognition and
Computer Vision, World Scientific Publishing Co., 1998, ch.2.1
[7] A. Zizzari, U. Seiffert, B. Michaelis, G. Gademann, S. Swiderski,
"Detection of Tumor in Digital Images of the Brain" in Proc. IASTEC
International Conference Signal Processing Pattern Recognition &
Applications, Rhodes, Greece, 2001, pp. 132-137.
[8] P. Kruizinga, N. Petkov, S. E. Grigorescu "Comparison of Texture
Features Based on Gabor Filters," in Proc. 10th International
Conference on Image Analysis and Processing, Venice, Italy, 1999, pp.
142-147.
[1] H. van den Bergh, "Early Detection of Lung Cancer and the Role of
Endoscopic Fluorescence Imaging", Med. Laser Appl. 18: 20-26, pp 20-
26, 2003.
[2] E. Passalidou, "Early Detection of the Lung Cancer", Pneumon vol. 3,
issue 15, Sept-Dec 2002.
[3] N. Apostolou, M. Haritou, N. Lolis, D. Beldekis, A. Rasidakis, D.
Koutsouris, "Advanced Platform for Lung Cancer Detection in
Autofluorescence Bronchoscopy Images. Clinical Application at the
"Sotiria" Hospital, Hellas", J. Qual. Life Res., pg. 154-160, vol. 3, issue
2, 2005.
[4] R. C. Gonzalez, R. E. Woods, S. L. Eddins, Digital Image Processing
Using Matlab. New Jersey: Ed. Pearson Prentice Hall, 2004.
[5] R. C. Gonzalez, R. E. Woods, Digital Image Processing, Addison-
Wesley Publ. Co., 1993.
[6] M. Tuceryan, A. K. Jain, The Handbook of Pattern Recognition and
Computer Vision, World Scientific Publishing Co., 1998, ch.2.1
[7] A. Zizzari, U. Seiffert, B. Michaelis, G. Gademann, S. Swiderski,
"Detection of Tumor in Digital Images of the Brain" in Proc. IASTEC
International Conference Signal Processing Pattern Recognition &
Applications, Rhodes, Greece, 2001, pp. 132-137.
[8] P. Kruizinga, N. Petkov, S. E. Grigorescu "Comparison of Texture
Features Based on Gabor Filters," in Proc. 10th International
Conference on Image Analysis and Processing, Venice, Italy, 1999, pp.
142-147.
@article{"International Journal of Medical, Medicine and Health Sciences:57204", author = "P. Bountris and E. Farantatos and N. Apostolou", title = "Advanced Image Analysis Tools Development for the Early Stage Bronchial Cancer Detection", abstract = "Autofluorescence (AF) bronchoscopy is an
established method to detect dysplasia and carcinoma in situ (CIS).
For this reason the “Sotiria" Hospital uses the Karl Storz D-light
system. However, in early tumor stages the visualization is not that
obvious. With the help of a PC, we analyzed the color images we
captured by developing certain tools in Matlab®. We used statistical
methods based on texture analysis, signal processing methods based
on Gabor models and conversion algorithms between devicedependent
color spaces. Our belief is that we reduced the error made
by the naked eye. The tools we implemented improve the quality of
patients' life.", keywords = "Bronchoscopy, digital image processing, lung
cancer, texture analysis.", volume = "1", number = "9", pages = "517-6", }