Gudmundsson M, El-Kwae E A, Kabuka M R
IEEE Trans Med Imaging. 1998 Jun;17(3):469-74. doi: 10.1109/42.712136.
An algorithm is developed that detects well-localized, unfragmented, thin edges in medical images based on optimization of edge configurations using a genetic algorithm (GA). Several enhancements were added to improve the performance of the algorithm over a traditional GA. The edge map is split into connected subregions to reduce the solution space and simplify the problem. The edge-map is then optimized in parallel using incorporated genetic operators that perform transforms on edge structures. Adaptation is used to control operator probabilities based on their participation. The GA was compared to the simulated annealing (SA) approach using ideal and actual medical images from different modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound. Quantitative comparisons were provided based on the Pratt figure of merit and on the cost-function minimization. The detected edges were thin, continuous, and well localized. Most of the basic edge features were detected. Results for different medical image modalities are promising and encourage further investigation to improve the accuracy and experiment with different cost functions and genetic operators.
开发了一种算法，该算法基于使用遗传算法（GA）对边缘配置进行优化，来检测医学图像中定位良好、未碎片化的细边缘。与传统遗传算法相比，该算法增加了几项改进措施以提高其性能。边缘图被分割成相连的子区域，以减少解空间并简化问题。然后，使用对边缘结构执行变换的内置遗传算子对边缘图进行并行优化。自适应用于根据算子的参与度来控制算子概率。使用来自不同模态（包括磁共振成像（MRI）、计算机断层扫描（CT）和超声）的理想和实际医学图像，将遗传算法与模拟退火（SA）方法进行比较。基于普拉特品质因数和成本函数最小化进行了定量比较。检测到的边缘细、连续且定位良好。大部分基本边缘特征都被检测到了。不同医学图像模态的结果很有前景，并鼓励进一步研究以提高准确性，并尝试不同的成本函数和遗传算子。
