Three-Dimensional Registration of Freehand-Tracked Ultrasound to CT Images of the Talocrural Joint

Ultrasound Bone Segmentation: A Scoping Review of Techniques and Validation Practices

Ultrasound bone segmentation is an important yet challenging task for many clinical applications. Several works have emerged attempting to improve and automate bone segmentation, which has led to a variety of computational techniques, validation practices and applied clinical scenarios. We characterize this exciting and growing body of research by reviewing published ultrasound bone segmentation techniques. We review 56 articles in detail and categorize and discuss the image analysis techniques that have been used for bone segmentation. We highlight the general trends of this field in terms of clinical motivation, image analysis techniques, ultrasound modalities and the types of validation practices used to quantify segmentation performance. Finally, we present an outlook on promising areas of research based on the unaddressed needs for solving ultrasound bone segmentation.

Hip-Joint CT Image Segmentation Based on Hidden Markov Model with Gauss Regression Constraints

Hip-joint CT images have low organizational contrast, irregular shape of boundaries and image noises. Traditional segmentation algorithms often require manual intervention or introduction of some prior information, which results in low efficiency and is unable to meet clinical needs. In order to overcome the sensitivity of classical fuzzy clustering image segmentation algorithm to image noise, this paper proposes a fuzzy clustering image segmentation algorithm combining Gaussian regression model (GRM) and hidden Markov random field (HMRF). The algorithm uses the prior information to regularize the objective function of the fuzzy C-means, and then improves it with KL information. The HMRF model establishes the neighborhood relationship of the label field by prior probability, while CRM model establishes the neighborhood relationship of feature field on the basis of the consistency between the central pixel label and its neighborhood pixel label. The experimental results show that the proposed algorithm has high segmentation accuracy.

A Review of Point Set Registration: From Pairwise Registration to Groupwise Registration

This paper presents a comprehensive literature review on point set registration. The state-of-the-art modeling methods and algorithms for point set registration are discussed and summarized. Special attention is paid to methods for pairwise registration and groupwise registration. Some of the most prominent representative methods are selected to conduct qualitative and quantitative experiments. From the experiments we have conducted on 2D and 3D data, CPD-GL pairwise registration algorithm and JRMPC groupwise registration algorithm seem to outperform their rivals both in accuracy and computational complexity. Furthermore, future research directions and avenues in the area are identified.