IVR image enhancement with ICA domain shrinkage filter and multi-scale filter

使用 ICA 域收缩滤波器和多尺度滤波器增强 IVR 图像

• 批准号: 20700170
• 负责人: HAN Xianhua
• 金额: $ 1.08万
• 依托单位: Ritsumeikan University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Young Scientists (B)
• 财政年份: 2008
• 资助国家: 日本
• 起止时间: 2008 至 2009
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-20700170/
• 关键词: 医用画像処理; 画質改善; 独立成分分析; 縮退フィルタ; 雑音除去; 多重解像度フィルタ; IVR画像; DVBV法

This research project mainly focus on IVR image quality improvement and vessel enhancement. We developed an adaptive ICA shrinkage filter and multi-scale filter for Poisson noise reduction and vessel extraction, and then fusion these two developed filters to achieve high quality IVR images with distinctive vessel and less noise effectiveness. Therefore, we want to apply the developed algorithms for real IVR image enhancement for diagnosis application. The research achievements are as the following :(1) Developed multi-scale filter for Vessel extraction from IVR images, and explore the high-speed algorithm;(2) Proposed an adaptive ICA shrinkage filter for Poisson noise reduction in IVR images, and then fusion the two developed algorithm for IVR image enhancement;(3) Applied our developed algorithm to real IVR images for diagnosis application;(4) Adopted DVBV methods for quantitative evaluation of the enhanced IVR images by different algorithms. Evaluation results showed that the enhanced image by our proposed algorithm has the highest DV/BV rate.

本课题主要研究IVR图像质量改善和血管增强。我们开发了一个自适应伊卡收缩滤波器和多尺度滤波器的泊松降噪和血管提取，然后融合这两个开发的滤波器，以实现高质量的IVR图像与独特的血管和低噪声的有效性。因此，我们希望将开发的算法用于诊断应用的真实的IVR图像增强。主要研究成果如下：（1）提出了多尺度滤波器用于IVR图像血管提取，并探索了快速算法;（2）提出了一种自适应伊卡收缩滤波器用于IVR图像的泊松噪声去除，并将两种算法融合用于IVR图像增强;（3）将本文提出的算法应用于真实的IVR图像的诊断应用;（4）采用DVBV方法对不同算法增强的IVR图像进行定量评价。评价结果表明，我们提出的算法增强图像具有最高的DV/BV率。

项目成果

Noise Reduction and Signal Enhancement in IVR Images by ICA Shrinkage Filters and Multiscale Filters

通过 ICA 收缩滤波器和多尺度滤波器对 IVR 图像进行降噪和信号增强

• 发表时间: 2008
• 作者: K. Matsuo;X. Han;K. Shibata;Y. Mishina Y. Mukuta;Y. -W. Chen
• 通讯作者: Y. -W. Chen

A robust method based on ICA and mixture sparsity for edge detection in medical images

• DOI: 10.1007/s11760-009-0140-5
• 发表时间: 2011-03
• 期刊: Signal, Image and Video Processing
• 作者: X. Han;Yenwei Chen