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Vanithamani, R.
- Automated Nano-Technology Based Particulate Filters to a Clean Environment in Rooms of Biomedical Applications
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Authors
Affiliations
1 Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, IN
1 Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, IN
Source
ICTACT Journal on Microelectronics, Vol 7, No 3 (2021), Pagination: 1189-1192Abstract
The products or the process of production are highly sensitive to contamination on the environment. In this regard, manufacturing of semiconductors and their related application plays a major cause. The airborne concentration of particulates like chemical vapours or dust compounds can be found even an enclosed environment. This accounts for pollution in clean rooms for the purpose of biomedical applications. These rooms should be monitored highly and should be protected from harmful substances and contamination risks. In this paper, we clean the air that is entering the room that is filtered with nano-particle based filters to eliminate the dust. The air is recirculated via nano-particulate high efficiency particulate air filter. These filters connected with nano-materials absorbs the contaminants. The implantable particulate filters are the edges of the room enables a clean environment. The experimental testing is conducted using several nano-filters that is connected with power design circuits to automatically control the entire environment. The results achieved show that the presence of microbials in the room is cleaned effectively.Keywords
Airborne Particulates, Pollution, Nano-Particle Filters, Clean Environment.
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- Segmentation Of Carotid Artery From Intravascular Ultrasound (IVUS) Images Using Deep Learning Techniques For Plaque Identification
Abstract Views :230 |
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, IN
1 Department of Electronics and Communication Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 3 (2022), Pagination: 2638-2643Abstract
The carotid artery is the major artery that supplies blood to the brain, neck region, and face. The plaque deposition in these arteries is caused mainly due to the deposition of cholesterol, calcium, and other cellular debris carried along with the bloodstream. Hence identification of plaque is essential to avoid stroke and other diseases related to the heart. This paper proposes a deep learning-based segmentation algorithm for the identification of plaque in carotid artery using Intravascular Ultrasound (IVUS) images. To compare the performance of the proposed algorithm with the existing algorithms, evaluation metrics such as Jaccard Index (JI), Dice Similarity Coefficient (DC), and Hausdorff Distance (HD) are computed. From the results, it is observed that the proposed algorithm exhibited a high value with JI of 0.9562, DC of 0.9587, and HD of 4.8080.Keywords
Intravascular Ultrasound Image, Segmentation, Deep Learning, Jaccard Index, Hausdorff Distance, Dice CoefficientReferences
- Tadashi Araki, Nobutaka Ikeda, Devarshi Shukla, Narendra D Londhe, Vimal K Shrivastava, Sumit K Banchhor, Luca Saba, Ardrew Nicolaides, Shoaib Shafique, John R Laird and Jasjit S Suri, “A New Method for IVUS-based Coronary Artery Disease Risk Stratification: A Link between Coronary and Carotid Ultrasound Plaque Burdens”, Computer Methods and Programs in Biomedicine, Vol. 124, pp. 161-179, 2015.
- A. Chaudhry, M. Hassan, A. Khan, J.Y. Kim and T.A. Tuan, “Automatic Segmentation and Decision Making of Carotid Artery Ultrasound Images”, Proceedings of International Conference on Advances in Intelligent Systems and Computing, pp. 1-13, 2013.
- Hannah Sofian, C.M. Joel, Norliza Mohd Noor and Hassan Dao, “Segmentation and Detection of Media Adventitia Coronary Artery Boundary in Medical Imaging Intravascular Ultrasound Using Otsu thresholding”, Proceedings of International Conference on Bio Signal Analysis, Processing and Systems, pp. 1-13, 2015.
- S. Latha, D. Samiappan and P. Muthu, “Fully Automated Integrated Segmentation of Carotid Artery Ultrasound Images using DBSCAN and Affinity Propagation”, Journal of Medical and Biological Engineering, Vol. 41, pp. 260271, 2021.
- C. Loizou and Marios Pantzaris, “Atherosclerotic Carotid Plaque Segmentation in Ultrasound Imaging of the Carotid Artery”, Proceedings of International Conference on MultiModality Atherosclerosis Imaging and Diagnosis, pp. 233238, 2014.
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- S. Latha, Dhanalakshmi Samiappan and R. Kumar, “Carotid Artery Ultrasound Image Analysis: A Review of the Literature”, Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, pp. 1-8, 2020.
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- Z. Xia, Z. Zhaoyu and Z. Wang,“IVUS Image Segmentation using Superpixel-Wise Fuzzy Clustering and Level Set Evolution”, Applied Sciences, Vol. 43, No. 1, pp. 1-15, 2019.
- Xin Yang, Jiaoying Jin, Mengling Xu, Huihui Wu, Wanji He, Ming Yuchi and Mingyue Ding, “Ultrasound Common Carotid Artery Segmentation Based on Active Shape Model”, Computational and Mathematical Methods in Medicine, Vol. 2013, pp. 1-12, 2013.
- J. Yang, L. Tong and A. Basu, “IVUS-Net: An Intravascular Ultrasound Segmentation Network”, Proceedings of International Conference on Smart Multimedia, pp. 1-13, 2015.
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- J.E. Park, K. Jihoon, A. Pil and Y.H. Kim, “Deep Learning Segmentation of Lumen and Vessel on IVUS Images”, Journal of the American College of Cardiology, Vol. 77, No. 14, pp. 1-10, 2021.
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- An Improved Classification Of MR Images For Cervical Cancer Using Convolutional Neural Networks
Abstract Views :179 |
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Authors
Affiliations
1 Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, IN
1 Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 2 (2021), Pagination: 2605-2609Abstract
Cervical cancer is the biggest cause of death in the field of women gynaecology. Patient treatment outcomes are influenced by the stage and nodal status of their cancers as well as their tumour size and histological classes. In this paper, we develop a classification model using a state-of-art heuristic mechanism that enables the use of deep learning algorithm to classify the MRI image from the input cervical images. The classification is conducted with highly dense network that helps to reduce the errors during the testing process. The simulation is conducted in matlab to test the efficacy of the model and the results of simulation shows that the proposed method achieves higher grade of classification accuracy than the other existing methods.Keywords
Classification, MR Image, Cervical Cancer, CNNReferences
- A. Ghoneim, G. Muhammad and H.S. Hossain, “Cervical Cancer Classification using Convolutional Neural Networks and Extreme Learning Machines”, Future Generation Computer Systems, Vol. 102, pp. 643-649, 2020.
- S. Karthick, P.A. Rajakumari and R.A. Raja, “Ensemble Similarity Clustering Frame work for Categorical Dataset Clustering using Swarm Intelligence”, Proceedings of International Conference on Intelligent Computing and Applications, pp. 549-557, 2021.
- N.V. Kousik and M. Saravanan, “A Review of Various Reversible Embedding Mechanisms”, International Journal of Intelligence and Sustainable Computing, Vol. 1, No. 3, pp. 233-266, 2021.
- A. Khadidos, A.O. Khadidos, S. Kannan and G. Tsaramirsis, “Analysis of COVID-19 Infections on a CT Image using Deep Sense Model”, Frontiers in Public Health, Vol. 8, pp. 1-20, 2020.
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- A Quick Algorithm for QRS Noise Detection in ECG Based on Discrete Wavelet Transform
Abstract Views :198 |
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Authors
Affiliations
1 Department of Electronics and Instrumentation Engineering, V. M .K. V. Engineering College, Salem, IN
2 Department of Electronics and Communication Engineering, G. C. E., Salem, IN
1 Department of Electronics and Instrumentation Engineering, V. M .K. V. Engineering College, Salem, IN
2 Department of Electronics and Communication Engineering, G. C. E., Salem, IN