29 April 2005 Automatic registration of ICG images using mutual information and perfusion analysis
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Introduction: Indocyanin green fundus angiographic images (ICGA) of the eyes is useful method in detecting and characterizing the choroidal neovascularization (CNV), which is the major cause of the blindness over 65 years of age. To investigate the quantitative analysis of the blood flow on ICGA, systematic approach for automatic registration of using mutual information and a quantitative analysis was developed. Methods: Intermittent sequential images of indocyanin green angiography were acquired by Heidelberg retinal angiography that uses the laser scanning system for the image acquisition. Misalignment of each image generated by the minute eye movement of the patients was corrected by the mutual information method because the distribution of the contrast media on image is changing throughout the time sequences. Several region of interest (ROI) were selected by a physician and the intensities of the selected region were plotted according to the time sequences. Results: The registration of ICGA time sequential images is required not only translate transform but also rotational transform. Signal intensities showed variation based on gamma-variate function depending on ROIs and capillary vessels show more variance of signal intensity than major vessels. CNV showed intermediate variance of signal intensity and prolonged transit time. Conclusion: The resulting registered images can be used not only for quantitative analysis, but also for perfusion analysis. Various investigative approached on CNV using this method will be helpful in the characterization of the lesion and follow-up.
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Namkug Kim, Namkug Kim, Jong-Mo Seo, Jong-Mo Seo, June-goo Lee, June-goo Lee, Jong Hyo Kim, Jong Hyo Kim, Kwangsuk Park, Kwangsuk Park, Hyeong-Gon Yu, Hyeong-Gon Yu, Young Suk Yu, Young Suk Yu, Hum Chung, Hum Chung, } "Automatic registration of ICG images using mutual information and perfusion analysis", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); doi: 10.1117/12.595107; https://doi.org/10.1117/12.595107

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