A Morphological Image Preprocessing Method Based on the Geometrical Shape of Lesions to Improve the Lesion Recognition Performance of Convolutional Neural Networks

Abstract

Convolutional neural networks (CNNs) play an important role in computer vision-related tasks for medical imaging. However, the quality of raw images in the dataset can be insufficient for the CNN model to learn the features of the target object. When the input image contains a complex background, the CNN model focuses on regions that are not essential for lesion recognition, such as background structures, leading to less accurate output prediction. This paper shows that this problem can be efficiently solved by an image preprocessing method based on mathematical morphology, which uses a priori knowledge about the lesion shape. The proposed method consists of h-dome transformation based on the geometrical shape information of the lesion region, and subsequent image histogram-modification processes, and has the ability to selectively enhance the lesion region from the background. This allows for the creation of images that explicitly represent the important region to be learned by the CNN model. Experiments on pulmonary nodule classification in chest x-ray images and skin lesion region segmentation in dermatoscopic images demonstrate that the CNN models trained on the preprocessed dataset created by the proposed method achieve remarkable performance improvements compared to the CNN models trained on the original dataset.