Abstract
The role of preprocessing and segmentation are vital in image processing and computer vision. The medical images are prone to noise and the filtering algorithms are used for noise removal. In this paper, the fast bilateral filter is employed for noise removal and it has good edge preservation capacity. The segmentation algorithms are used to extract the region of interest and edge detection is a classical algorithm for tracing the contours of objects in an image. The canny edge detector is efficient when compared with the conventional edge detectors. The fast bilateral filter is proposed in this paper has the computation complexity of O(1) per pixel, while the classical bilateral filter has the computation complexity of O(W) operations per pixel, where W is the kernel size. The algorithms were implemented in Raspberry Pi using Open CV software package. The algorithms were tested on real time medical images.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Change history
20 June 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04149-5
References
Al-Muhit A, Islam MS, Othman M (2004) VLSI implementation of discrete wavelet transform (DWT) for image compression. In: 2nd International Conference on Autonomous Robots and Agents, December 13–15, 2004, Palmerston North, New Zealand
Corsonello P, Perri S, Zicari P, Cocorullo G (2005) Microprocessor-based FPGA implementation of SPIHT image compression subsystems. Microprocess Microsyst 29:299–305
Dasika G, Fan K, Mahlke S (2009) Power-efficient medical image processing using PUMA. In: Proceedings of the IEEE 7th symposium on application specific processors, SASP 2009, San Francisco, CA, USA, July 27–28, 2009
Dyer M, Gupta AK, Galin N (2005) Nios II processor-based hardware/software co-design of the JPEG2000 Standard. In: Nios II Embedded Processor Design Contest—Outstanding Designs 2005
Fry Thomas W, Hauck Scott (2005) SPIHT image compression on FPGAs. IEEE Trans Circuits Syst Video Technol 15(9):1138–1147
Ilango G, Marudhachalam R (2011) New hybrid filtering techniques for removal of gaussian noise from medical images. ARPN J Eng Appl Sci 6(2):8–12
Jamil U, Sajid A, Hussain M (2019) Melanoma segmentation using bio-medical image analysis for smarter mobile healthcare. J Ambient Intell Hum Comput 10:4099–4120
Klimesh M, Stanton V, Watola D (2000) Hardware implementation of a lossless image compression algorithm using a field programmable gate array, The telecommunications and mission operations progress report. TMO PR 42-144, October–December 2000
Kouanou AT, Tchiotsop D, Fozin TF, Mounmo B, Tchinda R (2018) Real-time image compression system using an embedded board. Sci J Circuits Syst Signal Process 7(4):81–86
Kucera R, Vlcek J, Vlcek K (1998) DSP implementation of image compression by multi resolutional analysis. Radioengineering 7(1):7–9
Lin A (2012) Hardware implementation of a real-time image data compression for satellite remote sensing. In: Escalante B (ed.) Remote sensing—advanced techniques and platforms. InTech, ISBN: 978-953-51-0652-4
Mosqueron R, Dubois J, Paindavoine M (2006) Embedded image processing/compression for high-speed CMOS sensor. In: 14th European signal processing conference (EUSIPCO 2006), Florence, Italy, September 4–8, 2006
Nagaraja L, Nithin D, Nagarjun RS, Anand NM, Murthy VS (2015) Vision-based text recognition using Raspberry Pi. In: IJCA Proceedings on National conference on power systems and industrial automation NCPSIA 2015(4):1–3, December 2015
NasirCheRosli A (2010) Embedded system for biometric identification. In: Ude A (ed) Robot vision. ISBN: 978-953-307-077-3, InTech, 2010, Available from: http://www.intechopen.com/books/robot-vision/embeddedsystem-for-biometric-identification. Accessed 19 Sept 2019
Nayyar A, Puri V (2015) Raspberry Pi—a small, powerful, cost effective and efficient form factor computer: a review. Int J Adv Res Comp Sci Softw Eng 5(12):720–737
Richardson M, Wallace S (2012) Getting started with Raspberry Pi, 1st edn. O’Reilly. ISBN 978-1-4493-4421-4
Shrivastava K, Mishra P (2010) Dual code compression for embedded systems. In: International Conference on (VLSI Design), January 2–7, 2010
Singh SN, Kumar J, Ranjan R, Panigrahi S (2009) Hardware image compression with FPGA. Int J Recent Trends Eng 2(8):33–35
Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Proceedings of IEEE international conference on computer vision
Uthayakumar C, Kamalraj S (2019) Ambient intelligence architecture of MRPM context based 12-tap further desensitized half band FIR filter for EEG signal. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-019-01237-x
Vincent A, Varghese G (2015) An advanced, robust binarization and character recognition in document images based a Raspberry Pi. ARPN J Eng Appl Sci 10(17):7620–7625
Xie X, Ge S, Xie M (2019) An improved industrial sub-pixel edge detection algorithm based on coarse and precise location. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-019-01232-2
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s12652-022-04149-5
About this article
Cite this article
Manikandan, L.C., Selvakumar, R.K., Nair, S.A.H. et al. RETRACTED ARTICLE: Hardware implementation of fast bilateral filter and canny edge detector using Raspberry Pi for telemedicine applications. J Ambient Intell Human Comput 12, 4689–4695 (2021). https://doi.org/10.1007/s12652-020-01871-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-01871-w