Grayscale histogram feature extraction algorithm and its application in medical image analysis
Pages 43 - 47
Abstract
This paper applied grayscale histogram feature extraction to analyse a medical image of tuberculosis lungs. Five different edge detection methods, namely Roberts, Sobel, Prewitt, Canny, and LoG algorithms were used to realise the feature extraction of the medical image and their performace were compared. This study finds that Roberts and Prewitt algorithms were able to extract most information from the medical image, followed by Sobel and Canny algorithms, while LoG algorithm was able to extract the least information from the medical image. The results show that the features extracted by the algorithms can be used for pathological analysis.
References
[1]
Y. M. Y. Abdallah and T. Alqahtani. 2019. Research in Medical Imaging Using Image Processing Techniques. IntechOpen. https://doi.org/10.5772/intechopen.84360
[2]
Jason D. Bakos. 2016. Chapter 4 - Memory optimization and video processing. In Embedded Systems, Jason D. Bakos (Ed.). Morgan Kaufmann, Boston, 147–185. https://doi.org/10.1016/B978-0-12-800342-8.00004-3
[3]
İ Dinç, S Dinç, M Sigdel, M S Sigdel, R S Aygün, and M L Pusey. 2012. DT-Binarize: A decision tree based binarization for protein crystal images. Elsevier Inc., Chapter 12, 183–199. https://doi.org/10.1016/B978-0-12-802045-6.00012-0
[4]
L. Fan, F. Zhang, H. Fan, and Zhang C.2019. Brief review of image denoising techniques. Visual Computing for Industry, Biomedicine, and Art 2 (2019), 7. https://doi.org/10.1186/s42492-019-0016-7
[5]
T Liu, E Siegel, and D Shen. 2022. Deep Learning and Medical Image Analysis for COVID-19 Diagnosis and Prediction. Annu Rev Biomed Eng 24(2022), 179–201. https://doi.org/10.1146/annurev-bioeng-110220-012203
[6]
Simone Ferlin Oliveira, Karl Fürlinger, and Dieter Kranzlmüller. 2012. Trends in Computation, Communication and Storage and the Consequences for Data-intensive Science. In 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems. 572–579. https://doi.org/10.1109/HPCC.2012.83
[7]
M Puttagunta and S Ravi. 2021. Medical image analysis based on deep learning approach. Multimedia Tools and Applications 80, 16 (2021), 24365–24398. https://doi.org/10.1007/s11042-021-10707-4
[8]
A Saihood, H Karshenas, and A.R.N. Nilchi. 2022. Deep fusion of gray level co-occurrence matrices for lung nodule classification. PLoS One 17, 9 (2022), e0274516. https://doi.org/10.1371/journal.pone.0274516
[9]
Akshita Saini and Mantosh Biswas. 2019. Object Detection in Underwater Image by Detecting Edges using Adaptive Thresholding. In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). 628–632. https://doi.org/10.1109/ICOEI.2019.8862794
[10]
Wei Xu, Jinping Li, and Hongwei Jia. 2019. The Applications of the Edge Detection on Medical Diagnosis of Lungs. Journal of Physics: Conference Series 1187, 5 (apr 2019), 052100. https://doi.org/10.1088/1742-6596/1187/5/052100
[11]
Ziqi Xu, Xiaoqiang Ji, Meijiao Wang, and Xiaobing Sun. 2021. Edge detection algorithm of medical image based on Canny operator. Journal of Physics: Conference Series 1955, 1 (jun 2021), 012080. https://doi.org/10.1088/1742-6596/1955/1/012080
[12]
Li Yao and Sohail Muhammad. 2019. A novel technique for analysing histogram equalized medical images using superpixels. Computer Assisted Surgery 24, sup1 (2019), 53–61. https://doi.org/10.1080/24699322.2018.1560100
Index Terms
- Grayscale histogram feature extraction algorithm and its application in medical image analysis
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Published: 02 March 2023
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- Research-article
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- Refereed limited
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- Ministry of Higher Education Malaysia
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CCEAI 2023
CCEAI 2023: 2023 7th International Conference on Control Engineering and Artificial Intelligence
January 28 - 30, 2023
Sanya, China
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