Cell-based two-region competition algorithm with a map framework for boundary delineation of a series of 2D ultrasound images.

To ensure the delineated boundaries of a series of 2-D images closely following the visually perceivable edges with high boundary coherence between consecutive slices, a cell-based two-region competition algorithm based on a maximum a posteriori (MAP) framework is proposed. It deforms the region boundary in a cell-by-cell fashion through a cell-based two-region competition process. The cell-based deformation is guided by a cell-based MAP framework with a posterior function characterizing the distribution of the cell means in each region, the salience and shape complexity of the region boundary and the boundary coherence of the consecutive slices. The proposed algorithm has been validated using 10 series of breast sonograms, including seven compression series and three freehand series. The compression series contains two carcinoma and five fibroadenoma cases and the freehand series contains two carcinoma and one fibroadenoma cases. The results show that >70% of the derived boundaries fall within the span of the manually delineated boundaries. The robustness of the proposed algorithm to the variation of regions-of-interest is confirmed by the Friedman tests and the p-values of which are 0.517 and 0.352 for the compression and freehand series groups, respectively. The Pearson's correlations between the lesion sizes derived by the proposed algorithm and those defined by the average manually delineated boundaries are all higher than 0.990. The overlapping and difference ratios between the derived boundaries and the average manually delineated boundaries are mostly higher than 0.90 and lower than 0.13, respectively. For both series groups, all assessments conclude that the boundaries derived by the proposed algorithm be comparable to those delineated manually. Moreover, it is shown that the proposed algorithm is superior to the Chan and Vese level set method based on the paired-sample t-tests on the performance indices at a 5% significance level.