Camargo’s Indexive Filtering based Affine Invariant Sliced Regression for Ulcer Prediction Using Wireless Capsule Endoscopy Images
Camargo’s Indexive Filtering based Affine Invariant Sliced Regression for Ulcer Prediction Using Wireless Capsule Endoscopy Images |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-3 |
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| Year of Publication : 2025 | ||
| Author : S. Bhuvaneswari, M. Sulthan Ibrahim |
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| DOI : 10.14445/22315381/IJETT-V73I3P138 | ||
How to Cite?
S. Bhuvaneswari, M. Sulthan Ibrahim, "Camargo’s Indexive Filtering based Affine Invariant Sliced Regression for Ulcer Prediction Using Wireless Capsule Endoscopy Images," International Journal of Engineering Trends and Technology, vol. 73, no. 3, pp. 540-553, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I3P138
Abstract
An ulcer is a sore or lesion that forms in the coating of Gastro Intestinal (GI) area. The most common ulcers develop in the small intestine and stomach. In GI, ulcers tract potentially lead to serious conditions like Crohn’s disease and ulcerative colitis. Conventionally, detection of ulcers in the GI tract involves endoscopy techniques, which are uncomfortable for patients, and these methods may not effectively visualise the small intestine area. Therefore, WCE is the essential diagnostic task for examining the GI tract. Conventionally, ulcer detection using machines and DL methods has been developed through early detection and treatment. However, achieving accurate ulcer detection with minimal time complexity is a significant challenge. A novel technique called Camargo’s Indexive Kuwahara Filtering Based Affine Invariant Sliced Regression (CIKF-AISR) is introduced to enhance accuracy and minimise time complexity. The proposed CIKF-AISR technique includes three major processes: image acquisition, preprocessing and feature extraction. First, the numbers of WCE images are collected from the dataset. After the image acquisition, preprocessing is carried out to eradicate noise and protect edges by applying an adaptive Camargo’s indexive Kuwahara filtering technique for image smoothing. This helps to reduce MSE and increase PSNR. The segmentation and feature extraction process is executed to minimise the time complexity of ulcer detection. Von Neumann locality segmentation is employed to segment the image into different regions and extract the ROI with the help of Canberra distance measure between the image pixels. Then, dissimilar features are extracted using the Affine invariant saliency Sliced regression method. After extracting the features, ulcer detection is performed with higher accuracy. Experimental evaluation is carried out on several factors. The analysed results indicate that the CIKF-AISR technique achieved better performance in accuracy, PSNR, and precision and less time compared to conventional methods.
Keywords
Adaptive Camargo’s indexive Kuwahara filtering technique, Affine invariant saliency, Sliced regression method, Ulcer detection, Von Neumann locality segmentation, Wireless capsule endoscopy images.
References
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