More attention is paid for the feature importance ranking (FIR), in particular when high-throughput features can be extracted for intelligent diagnosis and personalized medicine. A large number of FIR methods have been proposed, while few are integrated for comparison and real-life applications. In this study, a matlab toolbox is presented and a total of 30 algorithms are collected. Moreover, the toolbox is evaluated on a database of 163 ultrasound images. To each breast lesion, 15 features are handcrafted. And to Figure out an optimal subset of features for classification, all combinations of features are tested and linear support vector machine is used for the malignancy prediction of lesions annotated in ultrasound images. At last, the effectiveness of FIR is analyzed according to performance comparison. The toolbox is available (https://github.com/NicoYuCN/matFIR). In the future work, more FIR methods, feature selection methods and machine learning classifiers will be integrated.