a Anhui Key Laboratory of Detection Technology and Energy Saving Devices , Anhui Polytechnic University , Wuhu , China.
b School of Electrical Engineering , Anhui Polytechnic University , Wuhu , China.
Comput Assist Surg (Abingdon). 2017 Dec;22(sup1):186-199. doi: 10.1080/24699322.2017.1389397. Epub 2017 Oct 16.
The issue of an automated approach for detecting cervical cancer is proposed to improve the accuracy of recognition. Firstly, the cervical cancer histology source images are needed to use image preprocessing for reducing the impact brought by noise of images as well as the impact on subsequent precise feature extraction brought by irrelevant background. Secondly, the images are grouped into ten vertical images and the information of texture feature is extracted by Grey Level Co-occurrence Matrix (GLCM). GLCM is an effective tool to analyze the features of texture. The textures of different diseases in the source image of Cervical Cancer Histology (such as contrast, correlation, entropy, uniformity and energy, etc.) can all be obtained in this way. Thirdly, the image is segmented by using K-means clustering and Marker-controlled watershed Algorithm. And each vertical image is divided into three layers to calculate the areas of different layers. Based on GLCM and lesion area features, the tissues are investigated with segmentation by using Support Vector Machine (SVM) method. Finally, the results show that it is effective and feasible to recognize cervical cancer by automated approach and verified by experiment.
提出了一种自动化方法来检测宫颈癌，以提高识别的准确性。首先，需要使用图像处理对宫颈癌组织学源图像进行预处理，以减少图像噪声的影响以及对后续精确特征提取的影响。其次，将图像分成十个垂直图像，并通过灰度共生矩阵（GLCM）提取纹理特征信息。GLCM 是分析纹理特征的有效工具。可以通过这种方式获得宫颈癌组织学源图像中不同疾病的纹理（如对比度、相关性、熵、均匀性和能量等）。第三，使用 K-均值聚类和标记控制分水岭算法对图像进行分割。并将每个垂直图像分为三层，以计算不同层的面积。基于 GLCM 和病变面积特征，使用支持向量机（SVM）方法对组织进行分割。最后，实验结果表明，该方法通过自动化方法识别宫颈癌是有效和可行的。
