| Autor: |
Rani, K. Vijila, Prince, M. Eugine, Therese, P. Sujatha, Shermila, P. Josephin, Devi, E. Anna |
| Zdroj: |
Multimedia Tools & Applications; Mar2024, Vol. 83 Issue 9, p27217-27242, 26p |
| Abstrakt: |
Content-Based Medical Image Retrieval is used to extract meaningful information from a large number of medical images. To extract image texture characteristics, a novel medical image retrieval technique is presented. Images from Computed Tomography scans are used as input images. At first, the respective image is affected by some noise disturbance. To mitigate this and to improve the contrast of the pixels in the source image, and then a fractional Hartley transform is applied to eradicate the noise variation as well as reduce the image distortion. About this, a better-filtered output is determined. Then the hybrid feature extraction technique is utilized to extract the desirable features. After this Modified Weight-Brownian Motion Monarch Butterfly Optimization approach is exploited to alleviate the unwanted features from the huge amount of features and select certain desirable features. Finally, the similarity between the selected features is measured to detect and classify the medical images. The proposed technique has been more advantageous to increase the accuracy level as well as gradually minimizing the error rate. The proposed algorithms are implemented using the image processing toolbox in MATLAB 2019 platform. The performances are analyzed by the proposed method in terms of precision (99%), recall (83%), F-measure (90%), and accuracy (98.85%). [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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