Institute of Mathematics, Silesian University of Technology, Kaszubska 23, Gliwice 44-100, Poland; Department of Electric, Electronic and Informatics Engineering, University of Catania, Viale A. Doria 6, Catania 95125, Italy.
Department of Electric, Electronic and Informatics Engineering, University of Catania, Viale A. Doria 6, Catania 95125, Italy.
Comput Methods Programs Biomed. 2018 Jul;161:173-180. doi: 10.1016/j.cmpb.2018.04.025. Epub 2018 Apr 28.
In medical examinations doctors use various techniques in order to provide to the patients an accurate analysis of their actual state of health. One of the commonly used methodologies is the x-ray screening. This examination very often help to diagnose some diseases of chest organs. The most frequent cause of wrong diagnosis lie in the radiologist's difficulty in interpreting the presence of lungs carcinoma in chest X-ray. In such circumstances, an automated approach could be highly advantageous as it provides important help in medical diagnosis.
In this paper we propose a new classification method of the lung carcinomas. This method start with the localization and extraction of the lung nodules by computing, for each pixel of the original image, the local variance obtaining an output image (variance image) with the same size of the original image. In the variance image we find the local maxima and then by using the locations of these maxima in the original image we found the contours of the possible nodules in lung tissues. However after this segmentation stage we find many false nodules. Therefore to discriminate the true ones we use a probabilistic neural network as classifier.
The performance of our approach is 92% of correct classifications, while the sensitivity is 95% and the specificity is 89.7%. The misclassification errors are due to the fact that network confuses false nodules with the true ones (6%) and true nodules with the false ones (2%).
Several researchers have proposed automated algorithms to detect and classify pulmonary nodules but these methods fail to detect low-contrast nodules and have a high computational complexity, in contrast our method is relatively simple but at the same time provides good results and can detect low-contrast nodules. Furthermore, in this paper is presented a new algorithm for training the PNN neural networks that allows to obtain PNNs with many fewer neurons compared to the neural networks obtained by using the training algorithms present in the literature. So considerably lowering the computational burden of the trained network and at same time keeping the same performances.
在医学检查中，医生会使用各种技术，以便为患者提供对其实际健康状况的准确分析。常用的方法之一是 X 射线筛查。这种检查经常有助于诊断胸部器官的某些疾病。错误诊断的最常见原因在于放射科医生难以在胸部 X 光片中解释肺癌的存在。在这种情况下，自动化方法可能非常有利，因为它为医学诊断提供了重要帮助。
在本文中，我们提出了一种新的肺癌分类方法。该方法从计算每个像素的局部方差开始，通过计算原始图像中每个像素的局部方差，获得与原始图像大小相同的输出图像（方差图像）。在方差图像中找到局部最大值，然后使用这些最大值在原始图像中的位置找到肺部组织中可能的结节轮廓。然而，在这个分割阶段之后，我们发现了许多假结节。因此，为了区分真假结节，我们使用概率神经网络作为分类器。
我们的方法的性能是 92%的正确分类，敏感性为 95%，特异性为 89.7%。分类错误是由于网络混淆了假结节和真结节（6%）以及真结节和假结节（2%）。
许多研究人员已经提出了用于检测和分类肺结节的自动化算法，但这些方法无法检测低对比度结节，并且计算复杂度高，相比之下，我们的方法相对简单，但同时提供了良好的结果，可以检测低对比度结节。此外，本文还提出了一种用于训练 PNN 神经网络的新算法，该算法允许与使用文献中存在的训练算法获得的神经网络相比，获得具有更少神经元的 PNN。因此，大大降低了训练网络的计算负担，同时保持相同的性能。
