Edge based method for kidney segmentation in MRI scans

Al-Shamasneh, Ala’a R. and Jalab, Hamid A. and Alkahtani, Hend (2021) Edge based method for kidney segmentation in MRI scans. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12799. pp. 299-309. ISSN 03029743, DOI https://doi.org/10.1007/978-3-030-79463-7_25.

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The precise and proficient detection of the kidney boundary in low-contrast images is considered as the main difficulty in the detection of kidney boundary in MRI image. The exact identification of a kidney shape in medical images with decreased non-kidney components to acquire insignificant false edge detection is adequately vital for several applications in surgical planning and diagnosis. Low illumination, poor-contrast, image close to the non-uniform state of organs with missing lines, shapes, and edges are considered fundamental difficulties in kidney boundary detection in MRI images. Kidney image edge detection is a significant step in the segmentation procedure because the final appearance and nature of the segmented image depend greatly on the edge detection technique utilized. This study presented a new method of extracting kidney edges from low quality MRI images. The proposed method extracted the unique information of the pixels, which represent the contours of the kidney for segmenting the region. The experimental results on different low-quality kidney MR images showed that the proposed model be able to carry out the effective segmentation of kidney MRI images based on the use of kidney edge components while preserving kidney-segmented edge information from low-contrast MRI images. © 2021, Springer Nature Switzerland AG.

Item Type: Article
Funders: None
Uncontrolled Keywords: Edge-based method; Kidney segmentation; Medical imaging; MRI images
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine
T Technology > T Technology (General)
Divisions: Faculty of Computer Science & Information Technology > Department of Computer System & Technology
Depositing User: Ms Zaharah Ramly
Date Deposited: 25 Oct 2023 02:29
Last Modified: 25 Oct 2023 02:29
URI: http://eprints.um.edu.my/id/eprint/35592

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