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首页> 外文会议>Proceedings of the 17th IASTED international conference on robotics applications ; Proceedings of the 11th IASTED international conference on biomedical engineering >OPTIMIZATION METHOD TO ESTIMATE BREAST TUMOUR PARAMETERS
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OPTIMIZATION METHOD TO ESTIMATE BREAST TUMOUR PARAMETERS

机译：估计乳腺肿瘤参数的优化方法

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摘要

This paper presents a methodology to predict location, size and hyperactivity level of a breast tumor using temperature profile over the skin surface of the breast that may be captured by infrared thermography or numeric simulation. The estimation methodology includes an evolutionary technique based on artificial neural network (ANN), an optimization scheme based on pattern search algorithm (PSA) with linear constraints and a heat flow analysis on anatomic-accurate (realistic) breast model using finite element method (FEM). Laboratory generated datasets obtained from the FEM are applied to the ANN to associate underlying tumor with surface temperature of the model. The ANN training/testing results are in good agreement with those obtained from numeric method (FEM), thus validates the network performance. The PSA is applied for generation of solution vector sets (tumor parameters) within a given space and the solution sets are employed to produce simulated datasets using the trained ANN. The best solution set is determined by minimizing a cost function involving comparing the target temperature profiles (clinical data) to those obtained by simulation.
机译：本文提出了一种方法，可利用红外热成像或数值模拟捕获的乳房皮肤表面温度分布图来预测乳腺肿瘤的位置，大小和活动过度。估算方法包括基于人工神经网络（ANN）的进化技术，基于具有线性约束的模式搜索算法（PSA）的优化方案以及使用有限元方法（FEM）对解剖学准确的（真实）乳房模型进行的热流分析）。从FEM获得的实验室生成的数据集应用于ANN，以将潜在的肿瘤与模型的表面温度相关联。 ANN训练/测试结果与从数值方法（FEM）获得的结果非常一致，从而验证了网络性能。 PSA用于在给定空间内生成求解矢量集（肿瘤参数），并且使用经过训练的ANN生成求解数据集。通过将涉及目标温度曲线（临床数据）与通过仿真获得的温度曲线（临床数据）进行比较的成本函数最小化，可以确定最佳解决方案集。

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