Abstract
Automated detection and identification of abnormal cells in the human body is a critical application for medical image computing. Enhancement and de-noising of images remain challenging tasks and imperative steps for image analysis algorithms. Indeed, due to its early role in the process, the results of advanced operators for feature extraction will highly depend on the quality of enhanced image produced. Depending on the presence of different noise types, particular algorithms will respond better. This paper presents a comprehensive comparison between several linear and non-linear filters applied on fluorescence microscope images for the localization and counting of specific cancer phenotypes from mouth cell samples. The objective analysis proposed is evaluating the PSNR and Delta-SNR (the SNR to SNR measure between original images and filtered ones) for blood sample sequences taken from Cancer Research Malaysia. Thirty Fluorescence microscope images with low contrast and non-uniform illumination have been tested and analysed. Non-linear algorithms seem to show improved contrast and background removal abilities compared to linear blurring and approximating filters.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wienert S et al (2012) Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach. Sci Rep 2
Nejad ARM, Hossein-Zadeh, G-A, Zadeh HS (2007) Evaluating effects of imaging parameters on single cell detection in molecular MRI via simulation. In: ICSPC 2007. IEEE international conference on signal processing and communications 2007. IEEE
Wang Y et al (2008) Medical image processing by denoising and contour extraction. In: 2008 international conference on information and automation ICIA 2008. IEEE
Wilson SM, Bacic A (2012) Preparation of plant cells for transmission electron microscopy to optimize immunogold labeling of carbohydrate and protein epitopes. Nat Protoc 7(9):1716–1727
Shitong W, Min W (2006) A new detection algorithm (NDA) based on fuzzy cellular neural networks for white blood cell detection. Inf Technol Biomed IEEE Trans 10(1):5–10
Ke C. (2008) White blood cell detection using a novel fuzzy morphological shared-weight neural network. In: International symposium on computer science and computational technology, ISCSCT’08. IEEE
Cheng ED, Challa S, Chakravorty R (2009) Microscopic cell segmentation and dead cell detection based on cfse and pi images by using distance and watershed transforms. In: Digital image computing: techniques and applications, DICTA’09. IEEE
Massoudi A, Semenovich, D, Sowmya A (2012) Cell tracking and mitosis detection using splitting flow networks in phase-contrast imaging. In: 2012 Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE
Chiang, A-S et al. (2010) Automatic detection of antinuclear autoantibodies cells in indirect immunofluorescence images. In: 2010 3rd international conference on biomedical engineering and informatics (BMEI). IEEE
Malakooti MV, Tafti AP, Naji HR (2012) An efficient algorithm for human cell detection in electron microscope images based on cluster analysis and vector quantization techniques. In: 2012 second international conference on digital information and communication technology and it’s applications (DICTAP). IEEE
Li Y et al (2011) An improved detection algorithm based on morphology methods for blood cancer cells detection. J Comput Inf Syst 7(13):4724–4731
Griffin LD (2000) Mean, median and mode filtering of images. In: Proceedings of the royal society of london A: mathematical, physical and engineering sciences. The Royal Society
Nixon M (2008) Feature extraction & image processing. Academic Press
Ruikar S, Doye D (2010) Image denoising using wavelet transform. In: 2010 2nd international conference on mechanical and electrical technology (ICMET). IEEE
Bhat M, Patil T (2014) Adaptive clip limit for contrast limited adaptive histogram equalization (CLAHE) of medical images using least mean square algorithm. In: 2014 International conference on advanced communication control and computing technologies (ICACCCT). IEEE
Acknowledgments
Thanks for the support of Universiti Teknologi PETRONAS during this study through Graduate Assistantship scheme (GA) and STIRF—cost center (015-3AAC88). We would like to thank our collaborators in Cancer Research Malaysia team for providing us the dataset of fluorescence images and their sharing of precious knowledge and advice about the medical part.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
Mkayes, A.A., Walter, N., Saad, N.M., Faye, I., Cheong, S.C., Lim, K.P. (2017). Enhancement of Cell Visibility and Contrast for Fluorescence Microscope Images by Subjective and Objective Analysis of Several Visual Aspects. In: Ibrahim, H., Iqbal, S., Teoh, S., Mustaffa, M. (eds) 9th International Conference on Robotic, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 398. Springer, Singapore. https://doi.org/10.1007/978-981-10-1721-6_35
Download citation
DOI: https://doi.org/10.1007/978-981-10-1721-6_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-1719-3
Online ISBN: 978-981-10-1721-6
eBook Packages: EngineeringEngineering (R0)