Recognition of Kidney Glomerulus by Dynamic Programming Matching Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Effective Edge Detection Methodology for Medical Images Based on Texture Discrimination
ICAPR '09 Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition
On Edge Detection of X-Ray Images Using Fuzzy Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Lesion segmentation plays an important role in medical image processing and analysis. There exist several successful dynamic programming (DP) based segmentation methods for general images. In those methods, the gradient is used as an important factor in the cost function to attract the contours to the boundaries. Since medical images have their characteristics such as low contrast, blurred edges and high noises, the gradient operator cannot work well enough to achieve a satisfactory performance for boundary detection. We define the local intra-class variance and combine it with the dynamic programming method to replace the traditional gradient operation. Experiments on synthetic and X-ray images are carried out and the results are compared with Canny and fast multilevel fuzzy edge detection (FMFED) algorithms. It is demonstrated that the proposed method performs better on medical images with low contrast, blurred edges and high noises. In addition, 483 regions of interest of mammograms randomly extracted from DDSM of the University of South Florida are used to compare our proposed method with the plane-fitting and dynamic programming method (PFDP), and the normalized cut segmentation method (Ncut). The results demonstrate that our method is more accurate and robust than PFDP and Ncut.