Haiyue Li¹, Jing Xie², Jing Ke³, Ye Yuan¹, Xiaoyong Pan¹, Hongyi Xin⁴, Hongbin Shen^1,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 487-506, 2024, DOI:10.32604/cmc.2024.049640 - 18 July 2024

Abstract Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue. Convolutional neural network (CNN) and generative adversarial network (GAN) are pivotal in medical image registration. However, existing methods often struggle with severe interference and deformation, as seen in histological images of conditions like Cushing’s disease. We argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator in GAN. In this study, we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image registration. To… More >

Yanyun Jiang¹, Yuanjie Zheng^1,2,*, Xiaodan Sui¹, Wanzhen Jiao³, Yunlong He⁴, Weikuan Jia¹

Computer Systems Science and Engineering, Vol.36, No.3, pp. 537-549, 2021, DOI:10.32604/csse.2021.014578 - 18 January 2021

Abstract Multispectral imaging (MSI) technique is often used to capture images of the fundus by illuminating it with different wavelengths of light. However, these images are taken at different points in time such that eyeball movements can cause misalignment between consecutive images. The multispectral image sequence reveals important information in the form of retinal and choroidal blood vessel maps, which can help ophthalmologists to analyze the morphology of these blood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deep… More >

Yongjie Zhang^∗, Yiming Jing^∗, Xinghua Liang^∗, Guoliang Xu^†, Lei Dong^‡

Molecular & Cellular Biomechanics, Vol.9, No.3, pp. 213-226, 2012, DOI:10.3970/mcb.2012.009.213

Abstract A greyscale-based fully automatic deformable image registration algorithm, based on an optical flow method together with geometric smoothing, is developed for dynamic lung modeling and tumor tracking. In our computational processing pipeline, the input data is a set of 4D CT images with 10 phases. The triangle mesh of the lung model is directly extracted from the more stable exhale phase (Phase 5). In addition, we represent the lung surface model in 3D volumetric format by applying a signed distance function and then generate tetrahedral meshes. Our registration algorithm works for both triangle and tetrahedral… More >