ImFEATbox: a toolbox for extraction and analysis of medical image features.

PURPOSE: In medical imaging, the digital post-processing and analysis of acquired images has become an important research field. Topics include various applications of image processing and machine learning aiming to assist radiologists in their diagnostic work. A crucial step in successfully implementing such systems is finding appropriate mathematical descriptions to reflect characteristics of acquired images. Which features are the most meaningful ones strongly depends on the underlying scientific/diagnostic question and the image itself. This makes researching, implementing and testing features time-consuming and cost-intensive. In our work, we aim to address this issue by creating ImFEATbox, a publicly available toolbox to extract and analyze image features for a wide range of applications.

METHODS: To reduce the amount of time spent for choosing the right features, we provide an assortment of feature extraction algorithms which are suitable for a broad variety of medical image processing problems. The toolbox includes both global and local features as well as feature descriptors. While being primarily developed in MATLAB, the majority of our algorithms is also available in Python to enable access to a wider range of researchers.

RESULTS: We tested the applicability of ImFEATbox on an FDG-PET/CT data set of 12 patients diagnosed with lung cancer and an MRI data set of 50 patients with prostate lesions. Employing the implemented algorithms in an exemplary manner, we are able to demonstrate its potential for different scientific problems, e.g., show differences between features, indicate redundancies in extracted feature sets by means of a correlation analysis and training a SVM to distinguish between high-risk and low-risk prostate lesions.

CONCLUSION: ImFEATbox provides a variety of feature extraction algorithms suitable for a large number of post-processing and analysis applications in medical imaging. The toolbox is publicly available and can thus be beneficial to a wide range of researchers working on medical image analysis.