Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis
Edge detection methods are significant in medical imaging-assisted diagnosis. However, existing methods based on grayscale gradient computation still need to be optimized in practicality, especially in terms of actual visual quality and sensitivity to image contrast. To optimize the visualization an...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/963 |
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| author | Dang Li Patrick Cheong-Iao Pang Chi-Kin Lam |
| author_facet | Dang Li Patrick Cheong-Iao Pang Chi-Kin Lam |
| author_sort | Dang Li |
| collection | DOAJ |
| description | Edge detection methods are significant in medical imaging-assisted diagnosis. However, existing methods based on grayscale gradient computation still need to be optimized in practicality, especially in terms of actual visual quality and sensitivity to image contrast. To optimize the visualization and enhance the robustness of contrast changes, we propose the Contrast Invariant Edge Detection (CIED) method. CIED combines Gaussian filtering and morphological processing methods to preprocess medical images. It utilizes the three Most Significant Bit (MSB) planes and binary images to detect and extract significant edge information. Each bit plane is used to detect edges in 3 × 3 blocks by the proposed algorithm, and then the edge information from each plane is fused to obtain an edge image. This method is generalized to common types of images. Since CIED is based on binary bit planes and eliminates complex pixel operations, it is faster and more efficient. In addition, CIED is insensitive to changes in image contrast, making it more flexible in its application. To comprehensively evaluate the performance of CIED, we develop a medical image dataset and conduct edge image and contrast evaluation experiments based on these images. The results show that the average precision of CIED is 0.408, the average recall is 0.917, and the average F1-score is 0.550. The results indicate that CIED is not only more practical in terms of visual effects but also robust in terms of contrast invariance. The comparison results with other methods also confirm the advantages of CIED. This study provides a novel approach for edge detection within medical images. |
| format | Article |
| id | doaj-art-70508789cfa840f087c355a503643a96 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-70508789cfa840f087c355a503643a962025-01-24T13:21:30ZengMDPI AGApplied Sciences2076-34172025-01-0115296310.3390/app15020963Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image AnalysisDang Li0Patrick Cheong-Iao Pang1Chi-Kin Lam2Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macao 999078, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macao 999078, ChinaEdge detection methods are significant in medical imaging-assisted diagnosis. However, existing methods based on grayscale gradient computation still need to be optimized in practicality, especially in terms of actual visual quality and sensitivity to image contrast. To optimize the visualization and enhance the robustness of contrast changes, we propose the Contrast Invariant Edge Detection (CIED) method. CIED combines Gaussian filtering and morphological processing methods to preprocess medical images. It utilizes the three Most Significant Bit (MSB) planes and binary images to detect and extract significant edge information. Each bit plane is used to detect edges in 3 × 3 blocks by the proposed algorithm, and then the edge information from each plane is fused to obtain an edge image. This method is generalized to common types of images. Since CIED is based on binary bit planes and eliminates complex pixel operations, it is faster and more efficient. In addition, CIED is insensitive to changes in image contrast, making it more flexible in its application. To comprehensively evaluate the performance of CIED, we develop a medical image dataset and conduct edge image and contrast evaluation experiments based on these images. The results show that the average precision of CIED is 0.408, the average recall is 0.917, and the average F1-score is 0.550. The results indicate that CIED is not only more practical in terms of visual effects but also robust in terms of contrast invariance. The comparison results with other methods also confirm the advantages of CIED. This study provides a novel approach for edge detection within medical images.https://www.mdpi.com/2076-3417/15/2/963medical imageedge detectionbit planecontrast invariance |
| spellingShingle | Dang Li Patrick Cheong-Iao Pang Chi-Kin Lam Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis Applied Sciences medical image edge detection bit plane contrast invariance |
| title | Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis |
| title_full | Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis |
| title_fullStr | Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis |
| title_full_unstemmed | Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis |
| title_short | Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis |
| title_sort | contrast invariant edge detection a methodological advance in medical image analysis |
| topic | medical image edge detection bit plane contrast invariance |
| url | https://www.mdpi.com/2076-3417/15/2/963 |
| work_keys_str_mv | AT dangli contrastinvariantedgedetectionamethodologicaladvanceinmedicalimageanalysis AT patrickcheongiaopang contrastinvariantedgedetectionamethodologicaladvanceinmedicalimageanalysis AT chikinlam contrastinvariantedgedetectionamethodologicaladvanceinmedicalimageanalysis |