Efficient superpixel-based brain MRI segmentation using multi-scale morphological gradient reconstruction and quantum clustering

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dc.authorscopusidAmin Golzari Oskouei / 57207307861
dc.authorscopusidBahman Arasteh / 39861139000
dc.authorscopusidAsgarali Bouyer / 35177297800
dc.authorwosidAmin Golzari Oskouei / S-4622-2019
dc.authorwosidBahman Arasteh / AAN-9555-2021
dc.authorwosidAsgarali Bouyer / JOZ-6483-2023
dc.contributor.authorGolzari Oskouei, Amin
dc.contributor.authorAbdolmaleki, Nasim
dc.contributor.authorBouyer, Asgarali
dc.contributor.authorArasteh, Bahman
dc.contributor.authorShirini, Kimia
dc.date.accessioned2025-04-17T12:51:27Z
dc.date.available2025-04-17T12:51:27Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractSegmentation of brain MRI images is a fundamental task in medical image analysis. However, existing clustering methods often face significant challenges, including high computational complexity in calculating distances between cluster centers and pixels at each iteration, sensitivity to initial parameters and noise, and inadequate consideration of local spatial structures. This paper introduces an innovative method, Efficient Superpixel-Based Brain MRI Segmentation using Multi-Scale Morphological Gradient Reconstruction and Quantum Clustering, designed to address these challenges. The aim is to develop an efficient and robust segmentation technique that enhances accuracy while mitigating computational and parameter-related issues. To achieve this, we propose a multi-scale morphological gradient reconstruction operation that generates precise superpixel images, thereby improving the representation of local spatial features. These superpixel images are then used to compute histograms, effectively compressing the original color image data. Quantum clustering is subsequently applied to these superpixels using histogram parameters, leading to the desired segmentation outcomes. Experimental results demonstrate that our method outperforms state-of-the-art clustering techniques in terms of both segmentation accuracy and processing speed. These findings underscore the proposed approach's potential to overcome traditional methods' limitations, offering a promising solution for brain MRI segmentation in medical imaging.
dc.identifier.citationOskouei, A. G., Abdolmaleki, N., Bouyer, A., Arasteh, B., & Shirini, K. (2025). Efficient superpixel-based brain MRI segmentation using multi-scale morphological gradient reconstruction and quantum clustering. Biomedical Signal Processing and Control, 100, 107063.
dc.identifier.doi10.1016/j.bspc.2024.107063
dc.identifier.endpage14
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85206549416
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.bspc.2024.107063
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6258
dc.identifier.volume100
dc.identifier.wosWOS:001338916300001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorGolzari Oskouei, Amin
dc.institutionauthorArasteh, Bahman
dc.institutionauthorBouyer, Asgarali
dc.institutionauthoridAmin Golzari Oskouei / 0000-0003-2551-7105
dc.institutionauthoridBahman Arasteh / 0000-0001-5202-6315
dc.institutionauthoridAsgarali Bouyer / 0000-0002-4808-2856
dc.language.isoen
dc.publisherElsevier ltd
dc.relation.ispartofBiomedical signal processing and control
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBrain MRI Segmentation
dc.subjectLocal Spatial Structures
dc.subjectMedical Image Analysis
dc.subjectQuantum Clustering
dc.subjectSuperpixel-Based Segmentation
dc.titleEfficient superpixel-based brain MRI segmentation using multi-scale morphological gradient reconstruction and quantum clustering
dc.typeArticle

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