Computer-aided diagnosis of mammographic masses using local geometric constraint image retrieval

Abstract Computer-Aided Diagnosis of masses in mammograms is an important indicator of breast cancer. The use of retrieval systems in breast examination is gradually increasing. Hence, in mammographic mass retrieval, the method of exploiting the vocabulary tree framework and the inverted file has been proven to have high accuracy and excellent scalability. However, it only considered the features in each image as a visual word and ignored the spatial configurations of features, which greatly affects the retrieval performance. To overcome this drawback, we adopt the geometric verification method in mammographic mass retrieval. First, we obtain corresponding match features based on the vocabulary tree framework and the inverted file. Then, we grasp local similarity characteristics of deformations within the local regions by constructing the circle regions of corresponding pairs. Meanwhile, we quarter the circle to express the geometric relationship of local matches in the area and strictly generate the spatial encoding. Additionally, we rotate local matches in the area to control the strictness of geometric constraints. Finally, we verify geometric consistency to filter the false matches. The experimental results demonstrate that our method could significantly improve the retrieval accuracy with low computational cost.