A graph matching based automatic regional registration method for sequential mammogram analysis

This paper presents a method for associating regions of sequential mammograms automatically using graph matching. The graph matching utilises relative spatial relationships between the regions of a mammogram to establish regional correspondences between two mammograms. As a first step of the method, the mammogram is segmented into separate regions using an adaptive pyramid segmentation algorithm. This process produces both segmented regions of the mammogram and a graph. The nodes of the graph represent the segmented regions, and the lines represent the relationships between the regions. The regions are then filtered to remove undesired regions. To express the spatial relations between the regions, we use a fuzzy logic expression, which takes into account the characteristics of each region including the shape, size and orientation. The spatial relations between regions are utilised as weights of the graph. The backtrack algorithm is then used to find the common subgraph between two graphs. The proposed method is applied to 95 temporal pairs of mammograms. For each temporal mammogram pair, an average of 13.2 regions are matched. All region matches are classified as "good", "average", "poor" and "unknown" by one of the authors (FM) based on visual perception. 63.5% of region matches are identified as "good", and 23.6% as "average". The percentages of "poor" and "unknown" are 10.9% and 2% respectively. These results indicate that our registration method may be useful for establishing regional correspondence between sequential mammograms.