Abstract
The application of image enhancement technology to Wireless capsule Endoscopy (WCE) could extremely boost its diagnostic yield. WCE based detection inside gastrointestinal tract has been carried out over a great extent for the seek of the presence of any kind of etiology. However, the quality of acquired images during endoscopy degraded due to factors such as environmental darkness and noise. Hence, decrease in quality also resulted into poor sensitivity and specificity of ulcer and diagnosis. In this paper, a method based on color image enhancement through geometric mean filter and gamma correction is proposed. The developed method used geometric mean filtering to reduce Gaussian noise present in WCE images and achieved better quality images in contrast to arithmetic mean filtering, which has blurring effect after filtration. Moreover, Gamma correction has been applied to enhance small details, texture and contrast of the images. The results shown improved images quality in terms of SNR (Signal to Noise Ratio) and PSNR (Peak Signal to Noise Ratio) which is beneficial for automatic detection of diseases and aids clinicians to better visualize images and ease the diagnosis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chen, Y., Lee, J.: Ulcer detection in wireless capsule endoscopy video. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 1181–1184. ACM (2012)
Hwang, S., Celebi, M.E.: Polyp detection in wireless capsule endoscopy videos based on image segmentation and geometric feature. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 678–681. IEEE (2010)
Penna, B., et al.: A technique for blood detection in wireless capsule endoscopy images. In: Proc of the 17th European Signal Processing Conference (EUSIPCO 2009), pp. 1864–1868. Citeseer, Glasgow (2009)
Iddan, G., et al.: Wireless capsule endoscopy. Nature 405, 417 (2000)
(2014), http://www.givenimaging.com/en-us/Innovative-Solutions/Capsule-Endoscopy/Pillcam-SB/Pages/default.aspx (cited March 30, 2014)
Ge, Z.-Z., Hu, Y.-B., Xiao, S.-D.: Capsule endoscopy in diagnosis of small bowel Crohn’s disease. World Journal of Gastroenterology 10(9), 1349–1352 (2004)
Lee, D., Poon, A., Chan, A.: Diagnosis of small bowel radiation enteritis by capsule endoscopy. Hong Kong Medical Journal= Xianggang Yi Xue Za Zhi/Hong Kong Academy of Medicine 10(6), 419–421 (2004)
Xiang, X., Li, G.-L., Wang, Z.-H.: Low-complexity and high-efficiency image compression algorithm for wireless endoscopy system. Journal of Electronic Imaging 15(2), 023017-1–023017-15 (2006)
Li, B., Meng, M.Q.-H.: Wireless capsule endoscopy images enhancement via adaptive contrast diffusion. Journal of Visual Communication and Image Representation 23(1), 222–228 (2012)
Meng, M.-H., et al.: Wireless robotic capsule endoscopy: state-of-the-art and challenges. In: Fifth World Congress on Intelligent Control and Automation, WCICA 2004, pp. 5561–5565. IEEE (2004)
Woo, S.H., et al.: Small intestinal model for electrically propelled capsule endoscopy. Biomed. Eng. Online 10, 108 (2011)
Holms, A., Quach, A.: Complementary Metal-Oxide Semiconductor Sensors (2010)
Feng, L., et al.: Automatic image enhancement based on multi-scale image decomposition. In: Fifth International Conference on Graphic and Image Processing. International Society for Optics and Photonics (2014)
Bini, A., Bhat, M.: A nonlinear level set model for image deblurring and denoising. The Visual Computer, 1–15 (2013)
McAndrew, A.: An introduction to digital image processing with matlab notes for scm2511 image processing. School of Computer Science and Mathematics, pp. 1–264. Victoria University of Technology (2004)
Hanumantharaju, M., et al.: Adaptive color image enhancement based geometric mean filter. In: Proceedings of the 2011 International Conference on Communication, Computing & Security, pp. 403–408. ACM (2011)
Guan, X., et al.: An image enhancement method based on gamma correction. In: Second International Symposium on Computational Intelligence and Design, ISCID 2009, pp. 60–63. IEEE (2009)
Cheng, Y., Wang, Y., Hu, Y.: Image enhancement algorithm based on Retinex for Small-bore steel tube butt weld’s X-ray imaging. WSEAS Transactions on Mathematics 8(7), 279–288 (2009)
Liu, C., et al.: Gaussian fitting for carotid and radial artery pressure waveforms: comparison between normal subjects and heart failure patients. Bio-medical Materials and Engineering 24(1), 271–277 (2014)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Suman, S. et al. (2014). Image Enhancement Using Geometric Mean Filter and Gamma Correction for WCE Images. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham. https://doi.org/10.1007/978-3-319-12643-2_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-12643-2_34
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12642-5
Online ISBN: 978-3-319-12643-2
eBook Packages: Computer ScienceComputer Science (R0)