研究目的
To develop a simple algorithm for whole brain tumour segmentation, in particular, low and high-grade glioma.
研究成果
The proposed method based on multimodal PSO clustering is promising and better than common single-modal clustering for whole brain tumour segmentation. However, improvements are needed to overcome the limitations of intensity-only clustering for certain tumour structures.
研究不足
The proposed method is currently 2-dimensional and may not adequately segment certain tumour structures where intensity-only clustering is inadequate.
1:Experimental Design and Method Selection:
The study uses multimodal clustering (PSO and FCM) and level-set method for brain tumour segmentation.
2:Sample Selection and Data Sources:
The Multimodal Brain Tumour Segmentation (BRATS) Benchmark 2013 database is used, consisting of 10 low-grade glioma (LGG) and 20 high-grade glioma (HGG) patients’ MRI images.
3:List of Experimental Equipment and Materials:
MRI images from BRATS 2013 database.
4:Experimental Procedures and Operational Workflow:
The approach involves clustering (FCM and PSO) to determine the initial contour for the level-set method, followed by region selection, holes filling, and level-set segmentation.
5:Data Analysis Methods:
The dice score is used to measure the spatial overlap between manually segmented tumour and predicated tumour based on the tested approach.
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