Jeelani Haris, Liang Haoyi, Acton Scott T, Weller Daniel S
IEEE Trans Image Process. 2019 Jul;28(7):3451-3461. doi: 10.1109/TIP.2019.2897289. Epub 2019 Feb 4.
In this paper, we describe a novel enhancement method for images containing filamentous structures. Our method combines a gradient sparsity constraint with a filamentous structure constraint for the effective removal of clutter and noise from the background. The method is applied and evaluated on three types of data: 1) confocal microscopy images of neurons; 2) calcium imaging data; and 3) images of road pavement. We found that the images enhanced by our method preserve both the structure and the intensity details of the original object. In the case of neuron microscopy, we find that the neurons enhanced by our method are better correlated with the original structure intensities than the neurons enhanced by well-known vessel enhancement methods. Experiments on simulated calcium imaging data indicate that both the number of detected neurons and the accuracy of the derived calcium activity are improved. Applying our method to real calcium data, more regions exhibiting calcium activity in the full field of view were found. In road pavement crack detection, smaller or milder cracks were detected after using our enhancement method.
在本文中，我们描述了一种针对包含丝状结构的图像的新型增强方法。我们的方法将梯度稀疏性约束与丝状结构约束相结合，以有效去除背景中的杂波和噪声。该方法应用于三种类型的数据并进行评估：1）神经元的共聚焦显微镜图像；2）钙成像数据；3）路面图像。我们发现，通过我们的方法增强的图像保留了原始对象的结构和强度细节。在神经元显微镜检查的情况下，我们发现通过我们的方法增强的神经元与原始结构强度的相关性比通过著名的血管增强方法增强的神经元更好。对模拟钙成像数据的实验表明，检测到的神经元数量和导出的钙活性准确性均得到提高。将我们的方法应用于实际钙数据时，在全视野中发现了更多表现出钙活性的区域。在路面裂缝检测中，使用我们的增强方法后检测到了更小或更轻微的裂缝。
