Artificial neural networks for automatic segmentation and identification of nasopharyngeal carcinoma
Artificial neural networks for automatic segmentation and identification of nasopharyngeal carcinoma
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DOI：
10.1016/j.jocs.2017.03.026
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发表时间：
2017-07-01
影响因子：
3.3
通讯作者：
Abdullah, Mohamad Khir
中科院分区：
文献类型：
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作者：
Mohammed, Mazin Abed;Abd Ghani, Mohd Khanapi;Abdullah, Mohamad Khir
鼻咽癌（NPC）的诊断是一个具有挑战性的问题，尚未得到最佳解决。NPC具有复杂的结构，即使是专家医生也很难诊断。在过去的几十年里，许多研究人员已经建立了大量的研究工作，并使用了许多不同的技术方法。然而，解决上述问题的最佳解决方案非常复杂，需要创新的方法来找到最佳解决方案。该研究提出了一种新的自动分割和识别NPC的人工神经网络从显微镜图像，而无需人为干预，发展的最佳特征，对初步的NPC病例发现。为了在显微镜图像中得到准确的NPC区域，我们提出了一种新的NPC分割方法，它有三个主要的创新点。首先，在第一阶段中，在基于区域的颜色增强待标记的图像之后，将使用K均值聚类。其次，采用神经网络根据训练阶段选择合适的对象。第三，提取分割区域的纹理特征，用于分割。在鉴别方面，颜色特征已被用于卵巢肿瘤良恶性的鉴别诊断。研究结果表明：（1）提出了一种新的自适应后处理方法，用于NPC的检测;（2）确定并建立了NPC自动分割和识别的评价标准;（3）重点介绍了以下方法：基于区域生长的技术和K-均值聚类方法选择最佳区域和（4）通过将ANN和SVM分割结果与NPC自动分类相关联，评估了预期结果的效率。同时也表明纹理特征在良恶性鉴别中具有一定的附加价值。因此，我们可以使用所提出的系统，第一，作为指标，以诊断的情况下，第二，使用它作为一个支持工具，为医生，以支持他的决定。我们评估的有效性的框架，首先比较自动分割对手动，然后将建议的分割解决方案集成到一个分类框架，用于识别良性和恶性肿瘤。两种方法的实验结果均表明，该方法对感兴趣区域的有效分割率为88.03%，当采用基于人工神经网络技术的线推定（NPC分类）时，分割率提高到91.01%，分类准确率（灵敏度）为93.42%，特异度为90.01%。(C)2017爱思唯尔B. V.保留所有权利。
Nasopharyngeal Carcinoma (NPC) diagnostic is a challenging issue that have not been optimally solved. NPC has a complex structure which makes it difficult to diagnose even by an expert physician. Many researchers over the last few decades till now have established a lot of research efforts and used many methods with different techniques. However, the best solution to resolve the mentioned issue is very complex and needs innovative methods to find the optimal solutions. The study presents a novel automatic segmentation and identification for NPC by artificial neural networks from microscopy images without human intervention by developing the best characteristics towards preliminary NPC cases discovery. For getting accurate region of NPC in the microscope image, we propose a novel NPC segmentation method that has three major innovation points. First, K-means clustering will be used in the first stage after enhancing the image to be labelled in the regions based on their colour. Second, neural network has been employed to select the right object based on training stage. Third, texture feature for the segmented region will extract to ted to the segmentation. Regarding to the identification, the colour features have used to diagnose the ovarian tumours to the differential between benign and malignant. The findings outcome from this study have shown that: (1) A new adaptive method has been used as post-processing in detecting NPC, (2) Identified and established an evaluation criterion for automatic segmentation and identification of NPC cases, (3) Highlight the methods: based on region growing based technique and K-means clustering method for selecting the best region and (4) Assessed the efficiency of the anticipated results by associating ANN and SVM segmentation results, and automatic NPC classification. Also indicate that the texture features have some extra value or added value in separating benign from malignant. Therefore, we can use the proposed system, first, as indicator to diagnosis the case, second, use it as a support tool for the doctor to support his decision. We evaluated the effectiveness of the framework by firstly comparing the automatic segmentation against the manual, and then integrating the proposed segmentation solution into a classification framework for identifying benign and malignant tumour. Both test results show that the method is effective in segmentation the region of interest which is around 88.03% Consequently, this rate expanded to 91.01% when line presumption (NPC classification) based on ANN technique is employed with high level accuracy of classification (sensitivity) of 93.42% and specificity of 90.01%. (C) 2017 Elsevier B.V. All rights reserved.