Segmentation of Anterior and Posterior Chambers on UBM Images by Fuzzy Clustering

To assess various segmentation methods for the anterior and posterior chambers in ultrasonic biomicroscopy (UBM) images. UBM images were collected from 102 patients diagnosed with primary angle-closure glaucoma, and the corresponding intraocular pressure (IOP) values were measured. The UBM images are manually segmented using ImageJ software as the gold standard, while automatic segmentation employs six distinct methods: Otsu threshold, K-means, fuzzy C-means, robust self-sparse fuzzy clustering algorithm, spatial intuitionistic fuzzy C-means algorithm, and fast robust fuzzy C-means (FRFCM) algorithm. The segmentation results were used to quantify the anterior chamber depth and the area, perimeter, and height of the posterior chamber. Quantitative analyses of the accuracy and reliability of the segmentation quantification results were conducted using relative error and intraclass correlation coefficients (ICC). A total of 408 clear enough UBM images were used for segmentation. The ICC values of the quantitative results of the FRFCM method are 0.996, 0.970, 0.986, and 0.970, outperforming the other five segmentation methods. It excels in accuracy, reliability, precision comparable to manual techniques, and surpasses them in reproducibility and efficiency. Thus, it can be used for semi-automatic analysis of both anterior and posterior chamber regions in UBM images. Additionally, Spearman correlation analysis was performed to explore the relationship between IOPs and the four quantification results. The Spearman correlation coefficients between IOP and anterior chamber depth, posterior chamber area, posterior chamber perimeter, and posterior chamber height were -0.0991, -0.0043, -0.0321, and 0.0757, respectively. There is no significant correlation between IOP and anterior chamber depth, posterior chamber area, perimeter, or height.