A Parallel Markov Cerebrovascular Segmentation Algorithm Based on Statistical Model

For segmenting cerebral blood vessels from the time-of-flight magnetic resonance angiography （TOF-MRA） images accurately, we propose a parallel segmentation algorithm based on statistical model with Markov random field （MRF）. Firstly, we improve traditional non-local means filter with patch-based Fourier transformation to preprocess the TOF-MRA images. In this step, we mainly utilize the sparseness and self-similarity of the MRA brain images sequence. Secondly, we add the MRF information to the finite mixture mode （FMM） to fit the intensity distribution of medical images. We make use of the MRF in image sequence to estimate the proportion of cerebral tissues. Finally, we choose the particle swarm optimization （PSO） algorithm to parallelize the parameter estimation of FMM. A large number of experiments verify the high accuracy and robustness of our approach especially for narrow vessels. The work will offer significant assistance for physicians on the prevention and diagnosis of cerebrovascular diseases.

Isometric 3D Shape Partial Matching Using GD-DNA

Isometric 3D shape partial matching has attracted a great amount of interest, with a plethora of applicationsranging from shape recognition to texture mapping. In this paper, we propose a novel isometric 3D shape partial matchingalgorithm using the geodesic disk Laplace spectrum （GD-DNA）. It transforms the partial matching problem into the geodesicdisk matching problem. Firstly, the largest enclosed geodesic disk extracted from the partial shape is matched with geodesicdisks from the full shape by the Laplace spectrum of the geodesic disk. Secondly, Generalized Multi-Dimensional Scalingalgorithm （GMDS） and Euclidean embedding are conducted to establish final point correspondences between the partialand the full shape using the matched geodesic disk pair. The proposed GD-DNA is discriminative for matching geodesicdisks, and it can well solve the anchor point selection problem in challenging partial shape matching tasks. Experimentalresults on the Shape Retrieval Contest 2016 （SHREC＇16） benchmark validate the proposed method, and comparisons withisometric partial matching algorithms in the literature show that our method has a higher precision.

Skull ethnic classification by combining skull auxiliary image with deep learning

Background:China is a multi-ethnic country.It is of great significance for the skull identification to realize the skull ethnic classification through computers,which can promote the development of forensic anthropology and accelerate the exploration of national development.Methods:In this paper,the 3D skull model is transformed into 2D auxiliary image including curvature,depth and elevation information,and then the deep learning method of the 2D auxiliary image is used for ethnic classification.We construct a convolution neural network structure inspired by VGGNet16 which has achieved excellent performance on image classification.In order to optimize the network,Adam algorithm is adopted to avoid falling into local minimum,and to ensure the stability of the algorithm with regularization terms.Results:Experiments on 400 skull models have been conducted for ethnic classification by our method.We set different learning rates to compare the performance of the model,the highest accuracy of ethnic classification is 98.75%,which have better performance than other five classical neural network structures.Conclusions:Deep learning based on skull auxiliary image for skull ethnic classification is an automatic and effective method with great application significance.