- Author
- Farmaha Ihor (Lviv Polytechnic National University, Ukraine), Banaś Marian (AGH University of Science and Technology Kraków, Poland), Savchyn Vasyl (Lviv Polytechnic National University, Ukraine), Lukashchuk Bohdan (Lviv Polytechnic National University, Ukraine), Farmaha Taras (Danylo Halytsky Lviv National Medical University, Ukraine)
- Title
- Wound Image Segmentation Using Clustering Based Algorithms
- Source
- New Trends in Production Engineering, 2019, vol. 2(1) cz.II, s. 570-578, rys., tab., bibliogr. 10 poz.
- Issue title
- Part II: Selected Organizational Problems in the Mining Industry
- Keyword
- Maszyny i urządzenia, Segmentacja, Algorytmy
Machinery and equipment, Segmentation, Algorithms - Note
- streszcz., summ.
- Abstract
- Classic methods of measurement and analysis of the wounds on the images are very time consuming and inaccurate. Automation of this process will improve measurement accuracy and speed up the process. Research is aimed to create an algorithm based on machine learning for automated segmentation based on clustering algorithms Methods. Algorithms used: SLIC (Simple Linear Iterative Clustering), Deep Embedded Clustering (that is based on artificial neural networks and k-means). Because of insufficient amount of labeled data, classification with artificial neural networks can`t reach good results. Clustering, on the other hand is an unsupervised learning technique and doesn`t need human interaction. Combination of traditional clustering methods for image segmentation with artificial neural networks leads to combination of advantages of both of them. Preliminary step to adapt Deep Embedded Clustering to work with bio-medical images is introduced and is based on SLIC algorithm for image segmentation. Segmentation with this method, after model training, leads to better results than with traditional SLIC. (original abstract)
- Full text
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- Bibliography
-
- Jansen, S., Rachel, K. (2016). The Evolving Field of Wound Measurement Techniques: A Literature Review. [online] Available at: https://www.woundsresearch.com/article/evolving-field-wound-measurement-techniques-literature-review [Accessed 31 June 2019].
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- Nejati, H., Ghazijahani, H. and Abdollahzadeh, M. (2018). Fine-grained wound tissue analysis using deep neural network [online] Available at: https://arxiv.org/pdf/1802.10426.pdf [Accessed 31 June 2019].
- Lai, Z. and Deng, H. (2018) Medical Image Classification Based on Deep Features Extracted by Deep Model and Statistic Feature Fusion with Multilayer Perceptron. [online] Computational Intelligenceand Neuroscience Available at: https://www.hindawi.com/journals/cin/2018/2061516/ [Accessed 31 June 2019].
- Kumar, K., Reddy, B. (2014) Wound image analysis classifier for efficient tracking of wound healing status. [online] An International Journal. Available at: http://aircconline.com/sipij/V5N2/5214sipij02.pdf [Accessed 31 June 2019].
- Hemalatha, R., Thamizhvani, T. and Dhivya, A. (2018). Active Contour Based Segmentation Techniques for Medical Image Analysis. [online] Available at: https://www.intechopen.com/books/medical-and-biological-image-analysis/active-contour-based-segmentation-techniques-for-medical-image-analysis [Accessed 31 June 2019].
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- Cited by
- ISSN
- 2545-2843
- Language
- eng
- URI / DOI
- http://dx.doi.org/10.2478/ntpe-2019-0062
