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	Accurate Lung Nodule Segmentation With Detailed Representation Transfer and Soft Mask Supervision 期刊论文 OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 页码: 13 作者: Wang, Changwei; Xu, Rongtao; Xu, Shibiao; Meng, Weiliang; Xiao, Jun \| 收藏 \| 浏览/下载：16/0 \| 提交时间：2023/12/21 Detailed representation transfer lung nodules segmentation medical images segmentation soft mask
	Uncertainty Quantification in Medical Image Segmentation 会议论文 OAI收割 Chengdu, China, December 11-14, 2020 作者: Li HX(李海星); Luo HB(罗海波) \| 收藏 \| 浏览/下载：31/0 \| 提交时间：2021/11/21 medical images uncertainty quantification segmentation prostate MRI image
	Local difference-based active contour model for medical image segmentation and bias correction 期刊论文 OAI收割 IET Image Processing, 2019, 卷号: 13, 期号: 10, 页码: 1755-1762 作者: Niu, Yuefeng; Cao, Jianzhong \| 收藏 \| 浏览/下载：72/0 \| 提交时间：2019/09/25 image segmentation medical image processing two-phase model medical images LBDE model precise segmentation results comparative models local difference-based active contour model medical image segmentation bias correction local bias field difference estimation model smooth orthogonal basis functions clustering criterion function measured image local region accurate segmentation results level set evolution process
	Local difference-based active contour model for medical image segmentation and bias correction 期刊论文 OAI收割 IET IMAGE PROCESSING, 2019, 卷号: 13, 期号: 10, 页码: 1755-1762 作者: Niu, Yuefeng; Cao, Jianzhong \| 收藏 \| 浏览/下载：29/0 \| 提交时间：2020/06/05 image segmentation medical image processing two-phase model medical images LBDE model precise segmentation results comparative models local difference-based active contour model medical image segmentation bias correction local bias field difference estimation model smooth orthogonal basis functions clustering criterion function measured image local region accurate segmentation results level set evolution process
	Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China Wu Z.-G.; Wang M.-J.; Han G.-L. 收藏 \| 浏览/下载：79/0 \| 提交时间：2013/03/25 Being an efficient method of information fusion image fusion has been used in many fields such as machine vision medical diagnosis military applications and remote sensing.In this paper Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing including segmentation target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First the two original images are decomposed by wavelet transform. Then based on the PCNN a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore by this algorithm the threshold adjusting constant is estimated by appointed iteration number. Furthermore In order to sufficient reflect order of the firing time the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules the experiments upon Multi-focus image are done. Moreover comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.