Abstract
Tubular structure enhancement plays an utmost role in medical image segmentation as a pre-processing technique. In this work, an unsupervised 3D tubular structure segmentation technique is developed, which is mainly inspired by the idea of filter combination. Three well-known vessel filters, Frangi’s filter, the modified Frangi’s filter and the Multiscale Fractional Anisotropic Tensor (MFAT) filter, separately enhance the original images. Next, the enhanced images obtained using three different filters are combined. Different categories of vessel filters have the ability of complementarity, which is the main motivation of combining these three advanced filters. The combination of them ensures a high diversity of the enhancing results. Weighted mean and median ranking methods are used to conduct the operation of filter combination. Based on the optimized weights for all the three individual filters, fuzzy C-means method is then applied to segment the tubular structures. The proposed technique is tested on the public DRIVE and STARE datasets, the public synthetic vascular models (2011 and 2013 VascuSynth Sample), and real-patient Coronary Computed Tomography Angiography (CCTA) datasets. Experimental results demonstrate that the proposed technique outperforms the state-of-the-art filter combination-based segmentation methods. Moreover, our proposed method is able to yield better tubular structure segmentation results than that of each individual filter, which exhibits the superiority of the proposed method. In conclusion, the proposed method can be further used to facilitate vessel segmentation in medical practice.
Highlights
In medical image processing and segmentation, tubular structure enhancement plays an utmost role as a medical image pre-processing technique
The combination of them ensures a high diversity of the enhancing results
Different categories of vessel filters have the ability of complementarity, which is the main motivation of filter combination
Summary
The developed technique for vascular structure segmentation based on filter combination can be divided into three parts: (1) filter combination; (2) weight optimization; (3) tubular structure segmentation. The Hessian matrix of the input images are computed. The second step, i.e., image enhancement, is the major novelty of this work. Frangi’s filter, the modified Frangi’s filter and the MFAT filter separately enhance the original images. The enhanced images obtained using three different filters are combined. Weighted mean and median ranking methods are used to conduct the operation of filter combination. Based on the optimized weights for all the three individual filters, fuzzy C-means method is applied to segment the tubular structures
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