Yazar "Golzari Oskouei, Amin" seçeneğine göre listele

Listeleniyor 1 - 4 / 4
  • Küçük Resim
    Öğe
    Efficient superpixel-based brain MRI segmentation using multi-scale morphological gradient reconstruction and quantum clustering
    (Elsevier ltd, 2025) Golzari Oskouei, Amin; Abdolmaleki, Nasim; Bouyer, Asgarali; Arasteh, Bahman; Shirini, Kimia
    Segmentation 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.
  • Küçük Resim Yok
    Öğe
    Feature-weighted fuzzy clustering methods: An experimental review
    (Elsevier B.V., 2025) Golzari Oskouei, Amin; Samadi, Negin; Khezri, Shirin; Najafi Moghaddam, Arezou; Babaei, Hamidreza; Hamini, Kiavash; Fath Nojavan, Saghar; Bouyer, Asgarali; Arasteh, Bahman
    Soft clustering, a widely utilized method in data analysis, offers a versatile and flexible strategy for grouping data points. Most soft clustering algorithms assume that all the features present in the feature space of a dataset are of equal importance and neglect their degree of informativeness or irrelevance. Distinguishing between the relative importance of features in providing an optimal clustering structure has become a very challenging task. Many feature weighting methods have been proposed to deal with this problem in the field of soft clustering, which can broadly categorized into six major types: feature reduction-based, entropy-based, variance-based, membership-based, optimization-based, and meta-heuristic-based. This paper comprehensively reviews the most significant fuzzy clustering algorithms that employ feature weighting techniques. A taxonomy of the feature weighting-based fuzzy clustering algorithms is presented. Furthermore, all state-of-the-art approaches are implemented in Python and compared in terms of clustering performance by conducting various experimental evaluation schemes. In this comprehensive experimental analysis, 26 state-of-the-art clustering algorithms are evaluated on two synthetic and 18 benchmark UCI datasets based on Accuracy (ACC), Normalized Mutual Information (NMI), Precision (PR), Recall (RE), F1, Silhouette (SI) and Davies-Bouldin (DB) evaluation criteria. Moreover, the significance of the experimental comparisons is examined using Friedman and Holm's post-hoc statistical tests. The experimental analysis demonstrates the superior performance of variance-based feature weighting algorithms in most datasets. All the tested algorithms are implemented in Python, and the related source codes are shared publicly at https://github.com/Amin-Golzari-Oskouei/FWSCA. © 2024 Elsevier B.V.
  • Küçük Resim Yok
    Öğe
    Viewpoint-Based Collaborative Feature-Weighted Multi-View Intuitionistic Fuzzy Clustering Using Neighborhood Information
    (Elsevier B.V., 2025) Golzari Oskouei, Amin; Samadi, Negin; Tanha, Jafar; Bouyer, Asgarali; Arasteh, Bahman
    This paper presents an intuitionistic fuzzy c-means-based clustering algorithm for multi-view clustering, addressing key challenges such as noise sensitivity, outlier influence, and the distinct importance of views, features, and samples. Our proposed approach incorporates view weights, feature weights, sample weights, and neighborhood information into a novel objective function. Additionally, we introduce an effective initial cluster center selection strategy that enhances clustering robustness. The efficiency of the proposed method is evaluated using various clustering criteria (AR, NMI, RI, FMI, and JI). Moreover, the effect of each module of the algorithm on the general clustering performance is examined exclusively. Experimental results on various benchmark multi-view datasets demonstrate that our algorithm outperforms state-of-the-art methods in terms of clustering accuracy and stability. The source code of the proposed method is accessible at https://github.com/Amin-Golzari-Oskouei/VCoFWMVIFCM. © 2024 Elsevier B.V.
  • Küçük Resim
    Öğe
    Viewpoint-based collaborative feature-weighted multi-view intuitionistic fuzzy clustering using neighborhood information
    (Elsevier b.v., 2024) Golzari Oskouei, Amin; Samadi, Negin; Tanha, Jafar; Bouyer, Asgarali; Arasteh, Bahman
    This paper presents an intuitionistic fuzzy c-means-based clustering algorithm for multi-view clustering, addressing key challenges such as noise sensitivity, outlier influence, and the distinct importance of views, features, and samples. Our proposed approach incorporates view weights, feature weights, sample weights, and neighborhood information into a novel objective function. Additionally, we introduce an effective initial cluster center selection strategy that enhances clustering robustness. The efficiency of the proposed method is evaluated using various clustering criteria ( AR, NMI, RI, FMI, and JI ). Moreover, the effect of each module of the algorithm on the general clustering performance is examined exclusively. Experimental results on various benchmark multi-view datasets demonstrate that our algorithm outperforms state-of-the-art methods in terms of clustering accuracy and stability. The source code of the proposed method is accessible at https://github.com/Amin-Golzari-Oskouei/VCoFWMVIFCM.