Abstract: Mass segmentation plays a crucial role in Computer-Aided Diagnosis (CAD) systems on mammograms. Good segmentation result can better reflect the pathological characteristics of mass. And it can provide basis for the subsequent feature extraction and classification of suspicious region. At present, lots of literatures of mass segmentation have been proposed. Dynamic Programming (DP) based mass segmentation algorithm uses edge information as well as the priori knowledge of grey level and size information of mass. But the traditional DP based algorithm has low adaptability and robustness in mammograms. In order to overcome these shortcomings, an improved dynamic programming method was presented. Firstly, the Contrast Limited Adaptive Histogram Equalization (CLAHE) was used to improve contrast of Region of Interest (ROI) about mass. And the Gaussian mask was used to mask the surrounding tissues. Then, transform the ROI into polar coordinate and calculate the local cost matrix combining the edge, grey level and size information of mass. And the cumulative cost matrix was calculated using the local cost matrix. Finally, in the process of contour tracing based on dynamic programming, the contour supervision mechanism was introduced to avoid the contour departure, which is caused by the low contrast and the influence of surrounding tissues. The segmentation results of improved algorithm are compared with the traditional algorithm. The experimental results show that the introduction of Gaussian mask and contour supervision can effectively mask the surrounding tissue and avoid the contour departure. The proposed algorithm improved the segmentation accuracy. It is more robust than the conventional methods.
|
Received: 10 September 2009
|
|
|
|