A novel approach for color-balanced reference image selection for breast histology image normalization

Abstract

Histology image analysis plays a crucial role in pathological research and diagnosis. The effectiveness of Computer-Aided Detection procedures largely depends on the normalization of histology images to ensure precise diagnoses. On the other hand, the success of this normalization process is tied to the choice of an appropriate reference image, which acts as a standard for aligning and modifying the color and intensity of other images in the dataset. However, the lack of a universally reliable method for selecting an appropriate reference image remains a substantial obstacle in histology image analysis. In this direction, the study introduces an automated procedure for selecting a reference image that is designed to accurately represent the entire spectrum of the dataset or its specific class under investigation. The impact of the proposed method on the morphological and structural quality of normalized histology images is investigated using three established normalization techniques: Reinhard, Macenko, and Vahadane. Moreover, the study also utilized two strategies: the global reference image strategy, where a single reference image is chosen for the entire dataset, and the class-specific reference image strategy, which considers the unique characteristics of each class. The results demonstrate that the proposed automatic reference image selection procedure consistently outperforms random selection in both strategies, yielding higher metric scores (SSIM, QSSIM, and PCC). Furthermore, the class-specific reference image approach significantly improves the image quality compared to random selection. The proposed method enhances alignment and accuracy in histology image normalization by leveraging class-specific reference images, facilitating more reliable analysis in histopathology research and clinical applications.