A novel approach for quantification of time–intensity curves in a DCE-MRI image series with an application to prostate cancer

• The method for quantification of DCE time–intensity curve shape patterns is proposed.
• To the best of author's knowledge this is the first method to the considered problem which uses the supervised classification.
• The proposed method can be used to improve specificity of MRI in characterizing cancerous lesions.

This paper considers the problem of an automatic quantification of DCE-MRI curve shape patterns. In particular, the semi-quantitative approach which classifies DCE time–intensity curves into clusters representing the tree main shape patterns is proposed. The approach combines heuristic rules with the naive Bayes classifier. In particular, the descriptive parameters are firstly derived from pixel-by-pixel analysis of the DCE time intensity curves and then used to recognise the curves which without a doubt represent the three main shape patterns. These curves are next used to train the naive Bayes classifier intended to classify the remaining curves within the dataset. Results of applying the proposed approach to the DCE-MRI scans of patients with prostate cancer are presented and discussed. Additionally, the overall performance of the approach is estimated through the comparison with the ground truth results provided by the expert.