Hybrid segmentation of mass in mammograms using template matching and dynamic programming.

Rationale And Objectives: Accurate image segmentation for breast lesions is a critical step in computer-aided diagnosis systems. The objective of this study was to develop a robust method for the automatic segmentation of breast masses on mammograms to extract feasible features for computer-aided diagnosis systems.

Materials And Methods: The data set used in this study consisted of 483 regions of interest extracted from 328 patients. A hybrid method for segmenting breast masses was proposed on the basis of the template-matching and dynamic programming techniques. First, a template-matching technique was used to locate and obtain the rough region of masses. Then, on the basis of this rough region, a local cost function for dynamic programming was defined. Finally, the optimal contour was derived by applying dynamic programming as an optimization technique. The performance of this proposed segmentation method was evaluated using area-based and boundary distance-based similarity measures based on radiologists' manually marked annotations. A comparison with three different segmentation algorithms on the data set was provided.

Results: The mean overlap percentage for our proposed hybrid method was 0.727 ± 0.127, whereas those for Timp and Karssemeijer's dynamic programming method, Song et al's plane-fitting and dynamic programming method, and the normalized cut segmentation method were 0.657 ± 0.216, 0.636 ± 0.190, and 0.562 ± 0.199, respectively. All P values for the measure distribution of our proposed method and the other three algorithms were <.001.

Conclusions: A hybrid method based on the template-matching and dynamic programming techniques was proposed to segment breast masses on mammograms. Evaluation results indicate that the proposed segmentation method can improve the accuracy of mass segmentation compared to three other algorithms. The proposed segmentation method shows better performance and has great potential in improving the accuracy of computer-aided diagnosis systems in interpreting mammograms.