Image Analysis Department, 3DHISTECH Ltd., 1141 Budapest, Hungary.
Department of BioTech Research Center, Óbuda University, 1034 Budapest, Hungary.
Sensors (Basel). 2021 Oct 26;21(21):7085. doi: 10.3390/s21217085.
Image quality, resolution and scanning time are critical in digital pathology. In order to create a high-resolution digital image, the scanner systems execute stitching algorithms to the digitized images. Due to the heterogeneity of the tissue sample, complex optical path, non-acceptable sample quality or rapid stage movement, the intensities on pictures can be uneven. The evincible and visible intensity distortions can have negative effect on diagnosis and quantitative analysis. Utilizing the common areas of the neighboring field-of-views, we can estimate compensations to eliminate the inhomogeneities. We implemented and validated five different approaches for compensating output images created with an area scanner system. The proposed methods are based on traditional methods such as adaptive histogram matching, regression-based corrections and state-of-the art methods like the background and shading correction (BaSiC) method. The proposed compensation methods are suitable for both brightfield and fluorescent images, and robust enough against dust, bubbles, and optical aberrations. The proposed methods are able to correct not only the fixed-pattern artefacts but the stochastic uneven illumination along the neighboring or above field-of-views utilizing iterative approaches and multi-focal compensations.
图像质量、分辨率和扫描时间在数字病理学中至关重要。为了创建高分辨率的数字图像，扫描仪系统会对数字化图像执行拼接算法。由于组织样本的异质性、复杂的光路、不可接受的样本质量或快速的载物台移动，图片上的强度可能不均匀。不可见和可见的强度失真会对诊断和定量分析产生负面影响。利用相邻视场的公共区域，可以估计补偿量以消除不均匀性。我们实现并验证了用于补偿区域扫描系统创建的输出图像的五种不同方法。所提出的方法基于传统方法，如自适应直方图匹配、基于回归的校正以及最先进的方法，如背景和阴影校正 (BaSiC) 方法。所提出的补偿方法既适用于明场图像，也适用于荧光图像，并且足够稳健，可以抵抗灰尘、气泡和像差。所提出的方法不仅可以使用迭代方法和多焦点补偿来校正固定模式伪影，还可以校正沿相邻或上方视场的随机不均匀照明。
