ArticleInternational journal of biomedical imaging2019
Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations.
Article in International journal of biomedical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Artificial Intelligence-Based Approaches for Brain Tumor Segmentation in MRI: A Review.NMR in biomedicine · 2025Review
- Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review.Sensors (Basel, Switzerland) · 2025Review
- AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis.Journal of imaging informatics in medicine · 2025Article
- Machine learning and deep learning for brain tumor MRI image segmentation.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique.Bioengineering (Basel, Switzerland) · 2023Article
- A teacher-student framework with Fourier Transform augmentation for COVID-19 infection segmentation in CT images.Biomedical signal processing and control · 2023Article
- Measuring Shape Parameters of Pearls in Batches Using Machine Vision: A Case Study.Micromachines · 2022Article
- Value of Magnetic Resonance Images and Magnetic Resonance Spectroscopy in Diagnosis of Brain Tumors under Fuzzy C-Means Algorithm.Contrast media & molecular imaging · 2022Article
- Detection and Localization of Early-Stage Multiple Brain Tumors Using a Hybrid Technique of Patch-Based Processing, k-means Clustering and Object Counting.International journal of biomedical imaging · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Inference of tumor and edema areas from brain magnetic resonance imaging (MRI) data remains challenging owing to the complex structure of brain tumors, blurred boundaries, and external factors such as noise. To alleviate noise sensitivity and improve the stability of segmentation, an effective hybrid clustering algorithm combined with morphological operations is proposed for segmenting brain tumors in this paper. The main contributions of the paper are as follows: firstly, adaptive Wiener filtering is utilized for denoising, and morphological operations are used for removing nonbrain tissue, effectively reducing the method's sensitivity to noise. Secondly, K-means++ clustering is combined with the Gaussian kernel-based fuzzy C-means algorithm to segment images. This clustering not only improves the algorithm's stability, but also reduces the sensitivity of clustering parameters. Finally, the extracted tumor images are postprocessed using morphological operations and median filtering to obtain accurate representations of brain tumors. In addition, the proposed algorithm was compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity, and recall.
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Registered trials
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