集成相异性准则与熵率超像素的图像分割模型

Objective High-precision image segmentation is a key issue for biomedical image processing. It can aid to understand the anatomical information of biological tissues better. But, the segmentation precision is restricted by the non- uniformity of image intensity and noise-related issues in the process of magnetic resonance imaging (MRI). In addition, more image segmentation effects are constrained by information loss due to multi-modality and spatial neighborhood relations of medical images. Our research is focused on an image segmentation model in combination with dissimilarity criterion and entropy rate super-pixel. Method Our method is based on a segmentation model in the context of multi-modality feature fusion. This model is composed of three parts as mentioned below: 1) thanks to the entropy rate super-pixel segmentation algorithm (entropy rate super-pixel, ERS) , the multi-modality image is pre-segmented to obtain super-pixel blocks, and a new fusion algorithm is illustrated to renumber them, the super-pixel image is then established. The accurate segmentation of common areas in the tissue area is guaranteed in terms of multi-modality fusion-added, and the boundaries of the tissue area can be divided more accurately, and the overall segmentation accuracy is improved. 2) Each super-pixel block is illustrated by a node of the undirected image, and the feature vector is extracted by the gray value of each node. The correlation between nodes is judged by dissimilarity weight, and the feature sequence of adjacent nodes is constructed. The multi-modality and spatial neighborhood information develop the fineness of the boundary, the robustness of local area noise and intensity non-uniformity. Finally, the feature sequence is used as the input of bi-directional long/short-term memory model. To improve the segmentation accuracy, the cross entropy loss is used for training. Result Our method is compared to some popular algorithms in the context of BrainWeb, MRbrains and BraTS2017 datasets. The BrainWeb dataset is regarded as a simulation dataset based on brain anatomical structure, which contains MR images of Tl , T2 and PD. Compared to LSTM-MA (LSTM method with multi-modality and adjacency constraint) , our pixel accuracy (PA) is 98. 93% (1. 28% higher) and the Dice similarity coefficient (DSC) is 97.71% (2.8 higher). The MRbrains dataset contains the ground truth of brain-relevant MR images, which consists of MR image of Tl, Tl IR and FLAIR modalities. Our demonstration achieves 92. 46% in relation to PA metric and 84. 74% in terms of DSC metric, which are 0. 63% and 1. 44% higher than LSTM-MA. The dataset of BraTS2017 is related to the four modalities of MR image like Tl, T1CE, T2 and FLAIR. We choose three modalities of them (T1CE, T2 and FLAIR) . Final PA and DSC metrics are reached to 98. 80% and 99. 47% . Furthermore, our convergence speed is in comparison with some popular deep learning techniques like convolutional recurrent decoding network (CRDN) , semantic flow network (SFNet) and UNet + + . Our competitive convergence results can be obtained when the number of iterations is 40. Conclusion Our analysis can optimize current multi-modality features further. Multi-modality super-pixel blocks are fused by a new fusion algorithm, and a better feature sequence is constructed via the dissimilarity criterion. The final segmentation result is obtained through training and testing of the bidirectional long/short-term memory network. The experimental results show that our method can optimize the applications of image segmentation, and enhance the robustness of image intensity-oriented non-uniformity and noise-related.