Edge Based Method for Kidney Segmentation in MRI Scans

Al-Shamasneh, Ala’a R.; Jalab, Hamid A.; Alkahtani, Hend Khalid

doi:10.1007/978-3-030-79463-7_25

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…Traditional image segmentation techniques are divided into the following categories: graph-based, [9][10][11] edge-based, 12,13 clustering-based, 14,15 thresholdingbased, 16,17 etc. Out of these techniques, thresholdingbased are easy operation and have outstanding performance, which is successfully implemented in various application areas.…”

Section: Introductionmentioning

confidence: 99%

A modified threshold score‐based multilevel thresholding segmentation technique for brain magnetic resonance images using opposition‐based learning hybrid rice optimization algorithm

Song

et al. 2022

Int J Imaging Syst Tech

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In modern clinical diagnostics, magnetic resonance imaging (MRI) is frequently used for brain tumor detection because of its high resolution of soft tissues, which plays a crucial role in the prevention, detection, and treatment planning. Therefore, it is meaningful to obtain high‐quality MR images by automatic thresholding for aiding diagnosis. Most multilevel thresholding techniques are based on histograms. It is susceptible to the limitation of grayscale spatial distribution and is difficult to be used for MR images with variable and complex morphology. In this paper, a novel multilevel thresholding segmentation approach with a non‐histogram using a modified threshold score (MTS) is proposed. An opposition‐based learning hybrid rice optimization (OHRO) algorithm is used to reduce the computational cost of MTS for the purpose of optimizing the threshold search. The strategy of opposition‐based learning expands the space of feasible solutions and avoids the search from stalling. The proposed approach is evaluated through the Harvard Medical School's whole brain atlas dataset. Comparing the results with TS‐OHRO, Tsallis‐OHRO, Kapur‐OHRO, and Masi‐OHRO, MTS‐OHRO achieves better quantitative and qualitative outcomes which demonstrate that the application of MTS‐OHRO to MR images is effective and feasible.

show abstract

Section: Introductionmentioning

confidence: 99%

A modified threshold score‐based multilevel thresholding segmentation technique for brain magnetic resonance images using opposition‐based learning hybrid rice optimization algorithm

Song

et al. 2022

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

Edge Based Method for Kidney Segmentation in MRI Scans

Cited by 1 publication

References 11 publications

A modified threshold score‐based multilevel thresholding segmentation technique for brain magnetic resonance images using opposition‐based learning hybrid rice optimization algorithm

A modified threshold score‐based multilevel thresholding segmentation technique for brain magnetic resonance images using opposition‐based learning hybrid rice optimization algorithm

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