Fast interactive volume rendering method for adjustable vessel segmentation visualization

Medical diagnosis software and computer-assisted surgical systems often use segmented image data to help clinicians make decisions. The segmentation extracts the region of interest from the background, which makes the visualization clearer. However, no segmentation method can guarantee accurate results under all circumstances. As a result, the clinicians need a solution that enables them to check and validate the segmentation accuracy as well as displaying the segmented area without ambiguities. With the method presented in this paper, the real CT or MR image is displayed within the segmented region and the segmented boundaries can be expanded or contracted interactively. By this way, the clinicians are able to check and validate the segmentation visually and make more reliable decisions. After experiments with real data from a hospital, the presented method is proved to be suitable for efficiently detecting segmentation errors. The new algorithm uses new graphic processing uint （GPU） shading functions recently introduced in graphic cards and is fast enough to interact oil the segmented area, which was not possible with previous methods.

Optimized Deep Learning Model for Fire Semantic Segmentation

Recent convolutional neural networks(CNNs)based deep learning has significantly promoted fire detection.Existing fire detection methods can efficiently recognize and locate the fire.However,the accurate flame boundary and shape information is hard to obtain by them,which makes it difficult to conduct automated fire region analysis,prediction,and early warning.To this end,we propose a fire semantic segmentation method based on Global Position Guidance(GPG)and Multi-path explicit Edge information Interaction(MEI).Specifically,to solve the problem of local segmentation errors in low-level feature space,a top-down global position guidance module is used to restrain the offset of low-level features.Besides,an MEI module is proposed to explicitly extract and utilize the edge information to refine the coarse fire segmentation results.We compare the proposed method with existing advanced semantic segmentation and salient object detection methods.Experimental results demonstrate that the proposed method achieves 94.1%,93.6%,94.6%,95.3%,and 95.9%Intersection over Union(IoU)on five test sets respectively which outperforms the suboptimal method by a large margin.In addition,in terms of accuracy,our approach also achieves the best score.

Detecting Circles Using a Two-Stage Approach

We propose a two-stage method for detecting circular objects in this paper. In the first stage, curves are divided as linear segments or nonlinear segments. A least square estimator is used to find the estimated centers and radii of the nonlinear segments in the second stage. The found centers and radii are then evaluated to see if there exist circles in the nonlinear segments. Both of the broken and occluded circular objects are evaluated for the proposed method. From the experimental results, it is seen that the proposed method is efficient.

Optimization of a Segmented Filter with a New Error Diffusion Approach

The segmented filters, based on spectral cutting, proved their efficiency for the multi-correlation. In this article we propose an optimisation of this cutting according to a new error diffusion method.