Dynamic updating atlas for heart segmentation with a nonlinear field‐based model

Cai, Ken; Yang, Rongqian; Yue, Hongwei; Li, Lihua; Ou, Shanxing; F, Liu

doi:10.1002/rcs.1785

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…Entropy denotes information content, while fuzzy entropy denotes the degree of fuzzy concentration 28 . High fuzzy entropy in a certain region indicates that the ambiguity fluctuation near the pixel is large and belongs to the detail region.…”

Section: Methodsmentioning

confidence: 99%

Improving medical image fusion method using fuzzy entropy and nonsubsampling contourlet transform

Lin

Wang

et al. 2020

Int J Imaging Syst Tech

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Many types of medical images must be fused, as single‐modality medical images can only provide limited information due to the imaging principles and the complexity of human organ structures. In this paper, a multimodal medical image fusion method that combines the advantages of nonsubsampling contourlet transform (NSCT) and fuzzy entropy is proposed to provide a basis for clinical diagnosis and improve the accuracy of target recognition and the quality of fused images. An image is initially decomposed into low‐ and high‐frequency subbands through NSCT. The corresponding fusion rules are adopted in accordance with the different characteristics of the low‐ and high‐frequency components. The membership degree of low‐frequency coefficients is calculated. The fuzzy entropy is also computed and subsequently used to guide the fusion of coefficients to preserve image details. High‐frequency components are fused by maximizing the regional energy. The final fused image is obtained by inverse transformation. Experimental results show that the proposed method achieves good fusion effect based on the subjective visual effect and objective evaluation criteria. This method can also obtain high average gradient, SD, and edge preservation and effectively retain the details of the fused image. The results of the proposed algorithm can provide effective reference for doctors to assess patient condition.

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Section: Methodsmentioning

confidence: 99%

Improving medical image fusion method using fuzzy entropy and nonsubsampling contourlet transform

Lin

Wang

et al. 2020

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

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“…The conventional registration approach generally has limitation on the images with poor quality, or with large shape variations between individuals. Therefore, the single atlas approach was modified by a two-step registration algorithm (locally affine registration, and the free-form deformations) [19], [20], and an atlas dynamic update algorithm using a scheme of nonlinear deformation field [21]. Nevertheless, the single atlas is still insufficient to capture the wide anatomical variations of hearts.…”

Section: B Atlas-based Methodsmentioning

confidence: 99%

Accurate Segmentation of Heart Volume in CTA With Landmark-Based Registration and Fully Convolutional Network

Zhao

Chen

et al. 2019

IEEE Access

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Accurate delineation of cardiac structures in CTA images remains challenging, due to the similar appearance of different components, the highly variable shape and intensity among individuals, and especially the vicinity of heart volumes and backgrounds (including fat tissues, great vessels, and livers). Therefore, we proposed an accurate heart volume segmentation method with landmark-based registration (LMR) and 3D fully convolutional network (3D-FCN). First, we defined uniformity constrained landmarks in the training images and then trained a regression forest model (RFM) to detect these landmarks in the testing image. Second, the registration between the landmarks in each training image and the test image was performed by a shallow neural network, which guided the label propagation from atlases to the test image. After the label fusion with majority voting, we finally constructed a 3D-FCN to further refine the boundary voxels with low voting values. In 22 cardiac CTA images, we compared our method with multiatlas segmentation, active shape model, DeepMedic, LMR + DeepMedic, and the separately implemented LMR and 3D-FCN. The results demonstrated the superiority of our method for the segmentation of heart volumes, with the average accuracy, dice coefficient, and mean Hausdorff distance as 96.25%, 93.98%, and 2.12 voxels, respectively. Furthermore, the training efficiency of the constructed 3D-FCN was higher than that of the DeepMedic through the comparison of training time and convergence of loss. The proposed heart segmentation could provide an accurate region of interest (ROI) for not only the four heart chambers but also the major trunks of vessels and coronary arteries.

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Medical image fusion based on saliency and adaptive similarity judgment

Wang

Cai

2019

Pers Ubiquit Comput

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Dynamic updating atlas for heart segmentation with a nonlinear field‐based model

Cited by 4 publications

References 31 publications

Improving medical image fusion method using fuzzy entropy and nonsubsampling contourlet transform

Improving medical image fusion method using fuzzy entropy and nonsubsampling contourlet transform

Accurate Segmentation of Heart Volume in CTA With Landmark-Based Registration and Fully Convolutional Network

Medical image fusion based on saliency and adaptive similarity judgment

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