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Deep learning-based image registration (DLIR) has been widely developed, but it remains challenging in perceiving small and large deformations. Besides, the effectiveness of the DLIR methods was also rarely validated on the downstream tasks. In the study, a multi-scale complexity-aware registration network (MSCAReg-Net) was proposed by devising a complexity-aware technique to facilitate DLIR under a single-resolution framework. Specifically, the complexity-aware technique devised a multi-scale complexity-aware module (MSCA-Module) to perceive deformations with distinct complexities, and employed a feature calibration module (FC-Module) and a feature aggregation module (FA-Module) to facilitate the MSCA-Module by generating more distinguishable deformation features. Experimental results demonstrated the superiority of the proposed MSCAReg-Net over the existing methods in terms of registration accuracy. Besides, other than the indices of Dice similarity coefficient (DSC) and percentage of voxels with non-positive Jacobian determinant (|J(phi)|=<0), a comprehensive evaluation of the registration performance was performed by applying this method on a downstream task of multi-atlas hippocampus segmentation (MAHS). Experimental results demonstrated that this method contributed to a better hippocampus segmentation over other DLIR methods, and a comparable segmentation performance with the leading SyN method. The comprehensive assessment including DSC, |J(phi)|=<0, and the downstream application on MAHS demonstrated the advances of this method.
Ahmad et al. in their paper for the first time proposed to apply sharp function for classification of images. In continuation of their work, in this paper we investigate the use of sharp function as an edge detector through well known diffusion models. Further, we discuss the formulation of weak solution of nonlinear diffusion equation and prove uniqueness of weak solution of nonlinear problem. The anisotropic generalization of sharp operator based diffusion has also been implemented and tested on various types of images.
Automated evaluation of contact angles in a three-phase system of selective agglomeration in liquids
(2020)
This study aims to an automated evaluation of contact angles in a three-phase system of selective agglomeration in liquids. Wetting properties, quantified by contact angles, are essential in many industries and their processes. Selective agglomeration as a three-phase system consists of a suspension liquid, a heterogeneous solid phase and an immiscible binding liquid. It offers the chance of establishing more efficient separation processes because of the shape-dependent wetting properties of fine particles (size ≤ 10 µm). In the present paper, an experimental setup for contact angle measurements of fine particles based on the Sessile Drop Method is described. Moreover, a new algorithm is discussed, which can be used to automatically compute contact angles from image data captured by a high-speed camera. The algorithm uses a marker-based watershed transform to segment the image data into regions representing the droplet, the carrier plate coated by fine particles, and the background. The main idea is a parametric modelling approach forthe time-dependent droplet’s contour by an ellipse.
The results show that the development of the dynamic contact angles towards a static contact angle can be efficiently determined based on this novel technique. These findings are useful for a detailed discrimination of wetting properties of spherical and irregularly shaped particles as well as their wetting kinetics. Also, a better understanding of selective agglomeration processes will be promoted by this user-friendly method.