Rule-based fuzzy vector median filters for 3D phase contrast MRI segmentation

Abstract

Recent technological advances have contributed to the advent of phase contrast magnetic resonance imaging (PCMRI) as standard practice in clinical environments. In particular, decreased scan times have made using the modality more feasible. PCMRI is now a common tool for flow quantification, and for more complex vector field analyses that target the early detection of problematic flow conditions. Segmentation is one component of this type of application that can impact the accuracy of the final product dramatically. Vascular segmentation, in general, is a long-standing problem that has received significant attention. Segmentation in the context of PCMRI data, however, has been explored less and can benefit from object-based image processing techniques that incorporate fluids specific information. Here we present a fuzzy rule-based adaptive vector median filtering (FAVMF) algorithm that in combination with active contour modeling facilitates high-quality PCMRI segmentation while mitigating the effects of noise.The FAVMF technique was tested on 111 synthetically generated PC MRI slices and on 15 patients with congenital heart disease. The results were compared to other multi-dimensional filters namely the adaptive vector median filter, the adaptive vector directional filter, and the scalar low pass filter commonly used in PC MRI applications. FAVMF significantly outperformed the standard filtering methods (p < 0.0001). Two conclusions can be drawn from these results: a) Filtering should be performed after vessel segmentation of PC MRI; b) Vector based filtering methods should be used instead of scalar techniques.

An Automatic Segmentation Method for MRI Multiparametric Volumes

“…Some methods of image segmentation, based on fuzzy concept are reported in the literature [7]- [9]; in addition to fuzzy clustering [9], fuzzy rule-based methods [7], [10]- [12], fuzzy thresholding [8], [13]- [15], fuzzy Markov Random Fields [16]- [18], and fuzzy region growing [19], [20] been reported. Fuzzy connectedness can be defined in in terms of "intensity connectedness" [21] or in terms of "affinity" [22].…”

Fuzzy Image Segmentation Using Membership Connectedness

“…There are several image segmentation methods based on fuzzy concept reported in [2][3][4] among which fuzzy connectedness [5] and fuzzy clustering [4] are two wellknown techniques for this purpose. Moreover, fuzzy rule-based methods [2,[6][7][8], fuzzy thresholding [3,[9][10][11], fuzzy markov random field [12][13][14], and fuzzy region growing [15,16] are also reported for region-based fuzzy segmentation.…”