An introduction to double stain normalization technique for brain tumour histopathological images

Stain normalization is an image pre-processing method extensively used to standardize multiple variances of staining intensity in histopathology image analysis. Staining variations may occur for several reasons, such as unstandardized protocols while preparing the specimens, using dyes from different manufacturers, and varying parameters set while capturing the digital images. In this study, a double stain normalization technique based on immunohistochemical staining is developed to improve the performance of the conventional Reinhard’s algorithm. The proposed approach began with preparing a target image by applying the contrast-limited adaptive histogram equalization (CLAHE) technique to the targeted cells. Later, the colour distribution of the input image will be matched to the colour distribution of the target image through the linear transformation process. In this study, the power-law transformation was applied to address the over-enhancement and contrast degradation issues in the conventional method. Five quality metrics comprised of entropy, tenengrad criterion (TEN), mean square error (MSE), structural similarity index (SSIM) and correlation coefficient were used to measure the performance of the proposed system. The experimental results demonstrate that the proposed method outperformed all conventional techniques. The proposed method achieved the highest average values of 5.59, 3854.11 and 94.65 for entropy, TEN, and MSE analyses

Identification of Suitable Contrast Enhancement Technique for Improving the Quality of Astrocytoma Histopathological Images.

Contrast enhancement plays an important part in image processing. In histology, the application of a contrast enhancement technique is necessary since it can help pathologists in diagnosing the sample slides by increasing the visibility of the morphological and features of cells in an image. Various techniques have been proposed to enhance the contrast of microscopic images. Thus, this paper aimed to study the effectiveness of contrast enhancement techniques in enhancing the Ki67 images of astrocytoma. Three contrast enhancement techniques consist of contrast stretching, histogram equalization, and CLAHE techniques were proposed to enhance the sample images. The performance of each technique was compared by computing seven quantitative measures. The CLAHE technique was preferred for enhancing the contrast of the astrocytoma images. This technique produces good results especially in contrast enhancement, edge conservation and enhancement, brightness preservation, and minimum distortions to the enhanced images.