Sigdel Madhu S, Sigdel Madhav, Dinç Semih, Dinç İmren, Pusey Marc L, Aygün Ramazan S
Department of Computer Science, University of Alabama in Huntsville, Huntsville, Alabama 35899, United States.
iXpressGenes, Inc., 601 Genome Way, Huntsville, Alabama 35806, United States.
Proc IEEE Southeastcon. 2014 Mar;2014. doi: 10.1109/SECON.2014.6950754.
One of the difficulties for proper imaging in microscopic image analysis is defocusing. Microscopic images such as cellular images, protein images, etc. need properly focused image for image analysis. A small difference in focal depth affects the details of an object significantly. In this paper, we introduce a novel auto-focusing approach based on Harris Corner Response Measure (HCRM) and compare the performance with some existing auto-focusing methods. We perform our experiments on protein images as well as a simulated image stack to evaluate the performance of our method. Our results show that our HCRM-based technique outperforms other techniques.
显微图像分析中进行恰当成像的困难之一是散焦。诸如细胞图像、蛋白质图像等显微图像在进行图像分析时需要对焦合适的图像。焦深上的微小差异会显著影响物体的细节。在本文中，我们介绍一种基于哈里斯角点响应度量（HCRM）的新型自动对焦方法，并将其性能与一些现有的自动对焦方法进行比较。我们在蛋白质图像以及模拟图像堆栈上进行实验，以评估我们方法的性能。我们的结果表明，我们基于HCRM的技术优于其他技术。
