| dc.creator |
Mwawado, Rehema |
|
| dc.creator |
Maiseli, Baraka |
|
| dc.creator |
Dida, Mussa |
|
| dc.date |
2020-10-12T12:25:39Z |
|
| dc.date |
2020-10-12T12:25:39Z |
|
| dc.date |
2020-08 |
|
| dc.date.accessioned |
2022-10-25T09:15:58Z |
|
| dc.date.available |
2022-10-25T09:15:58Z |
|
| dc.identifier |
https://dspace.nm-aist.ac.tz/handle/20.500.12479/981 |
|
| dc.identifier.uri |
http://hdl.handle.net/123456789/94727 |
|
| dc.description |
This research article published by Engineering, Technology & Applied Science Research, Vol. 10, No. 4, 2020 |
|
| dc.description |
Segmentation is an open-ended research problem in
various computer vision and image processing tasks. This preprocessing operation requires a robust edge detector to generate
appealing results. However, the available approaches for edge
detection underperform when applied to images corrupted by
noise or impacted by poor imaging conditions. The problem
becomes significant for images containing diabetic foot ulcers,
which originate from people with varied skin color. Comparative
performance evaluation of the edge detectors facilitates the
process of deciding an appropriate method for image
segmentation of diabetic foot ulcers. Our research discovered
that the classical edge detectors cannot clearly locate ulcers in
images with black-skin feet. In addition, these methods collapse
for degraded input images. Therefore, the current research
proposes a robust edge detector that can address some limitations
of the previous attempts. The proposed method incorporates a
hybrid diffusion-steered functional derived from the total
variation and the Perona-Malik diffusivities, which have been
reported to can effectively capture semantic features in images.
The empirical results show that our method generates clearer
and stronger edge maps with higher perceptual and objective
qualities. More importantly, the proposed method offers lower
computational times—an advantage that gives more insights into
the possible application of the method in time-sensitive tasks. |
|
| dc.format |
application/pdf |
|
| dc.language |
en |
|
| dc.publisher |
Engineering, Technology & Applied Science Research |
|
| dc.subject |
Edge detection |
|
| dc.subject |
Execution time |
|
| dc.subject |
Diabetic foot ulcers |
|
| dc.subject |
Image segmentation |
|
| dc.title |
Robust Edge Detection Method for the Segmentation of Diabetic Foot Ulcer Images |
|
| dc.type |
Article |
|