Objective:

To design a set of novel and effective features to quantify the spiculation characteristic of the coronal image in automated breast volume scanner (ABVS)

in order to assist in classifying benign and malignant breast tumors.

Methods:

Firstly

the tumor regions on the coronal planes of ABVS images were automatically segmented. Secondly

the maximum energy maps with multi-scale and multi-angle filtering were obtained

and the thresholding and morphological processing on the energy maps were performed.

Then six features to describe the spiculation characteristic were extracted. Finally

we established a classifierand verified the effectiveness of the designed features in the tasks of recognizing the spiculation and classifying benign and malignant tumors.

Results:

The classification performances of the designed features by cross-validation on the data of 195 cases of breast tumors were evaluated. In the task of recognizing the spiculation

the area under curve (AUC) of the receiver operating characteristic (ROC) curve was between 0.83 and 0.89 when using only one of the six features

and 0.91 when using all six features. In the task of classifying benign and malignant tumors

the AUC was between 0.68 and 0.75 when using only one of the six features

and 0.75 when using all six features.

Conclusion:

The designed features can well quantify the speculation characteristic in the coronal plane of ABVS. It is of great significance in classifying benign and malignant tumors.