A machine learning approach to quantifying noise in medical images

As advances in medical imaging technology are resulting in significant growth of biomedical image data, new techniques are needed to automate the process of identifying images of low quality. Automation is needed because it is very time consuming for a domain expert such as a medical practitioner or a biologist to manually separate good images from bad ones. While there are plenty of de-noising algorithms in the literature, their focus is on designing filters which are necessary but not sufficient for determining how useful an image is to a domain expert. Thus a computational tool is needed to assign a score to each image based on its perceived quality. In this paper, we introduce a machine learning-based score and call it the Quality of Image (QoI) score. The QoI score is computed by combining the confidence values of two popular classification techniques—support vector machines (SVMs) and Naive Bayes classifiers. We test our technique on clinical image data obtained from cancerous tissue samples. We used 747 tissue samples that are stained by four different markers (abbreviated as CK15, pck26, E_cad and Vimentin) leading to a total of 2,988 images. The results show that images can be classified as good (high QoI), bad (low QoI) or ugly (intermediate QoI) based on their QoI scores. Our automated labeling is in agreement with the domain experts with a bi-modal classification accuracy of 94%, on average. Furthermore, ugly images can be recovered and forwarded for further post-processing.