%0 Journal Article
%@ 03029743
%A Suman, S.
%A Hussin, F.A.
%A Malik, A.S.
%A Walter, N.
%A Goh, K.L.
%A Hilmi, I.
%A Ho, S.H.
%D 2014
%F scholars:4825
%I Springer Verlag
%J Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
%K Endoscopy; Gaussian noise (electronic); Geometry; Signal to noise ratio; Textures, Arithmetic mean filtering; Color image enhancement; Gamma correction; Geometrical means filter; Image enhancement technologies; PSNR (peak signal to noise ratio); Sensitivity and specificity; Wireless capsule endoscopy, Image enhancement
%P 276-283
%R 10.1007/978-3-319-12643-2₃₄
%T Image enhancement using geometric mean filter and gamma correction for WCE images
%U https://khub.utp.edu.my/scholars/4825/
%V 8836
%X 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. © Springer International Publishing Switzerland 2014.
%Z cited By 21; Conference of 21st International Conference on Neural Information Processing, ICONIP 2014 ; Conference Date: 3 November 2014 Through 6 November 2014; Conference Code:109879