Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System
Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System
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DOI：
10.1016/j.procs.2015.08.228
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
S. N. Sulaiman;N. Isa;N. Othman;F. Ahmad
中科院分区：
文献类型：
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作者：
S. N. Sulaiman;N. Isa;N. Othman;F. Ahmad
宫颈癌每年都造成许多人死亡。筛查测试，如用于检测癌前阶段的巴氏涂片测试，能够避免宫颈癌的发生。然而，巴氏涂片测试有几个缺点，如不太有效的载玻片制备和人为错误。因此，一个计算机辅助诊断系统被引入作为一个解决问题的方法。已经建立的诊断系统之一是NeuralPap。然而，NeuralPap的性能受到几个约束的限制。本研究提出了几种新的图像处理算法，以减少这些限制。提出了自适应模糊k均值聚类算法（AFKM）来代替移动k均值聚类算法（MKM），将巴氏涂片图像分割为细胞核、细胞质和背景区域。其次，基于伪彩色的特征提取算法称为伪彩色特征提取（PCFE）手册和半自动PCFE的设计，以取代基于区域生长的特征提取（RGBFE），使用单色图像。本研究通过将颜色空间的概念与半自动PCFE算法相结合，提出了重叠细胞的特征提取算法，与NeuralPap系统相比向前迈进了一步。此外，本研究亦提出AFKM演算法作为一种新的径向基函数（RBF）与混合径向基函数（HRBF）网路中心定位演算法，以取代MKM演算法。整个算法已被证明比NeuralPap中使用的相应算法产生更好的性能。此外，所有算法的组合已成功将宫颈癌分类的准确性提高到76.35%，而之前的NeuralPap系统为73.40%。
Cervical cancer has caused many deaths each year. Screening tests, such as Pap smear test used for the detection of the precancerous stage are able to avoid the occurrence of cervical cancer. However, the Pap smear test has several disadvantages such as less effective slides preparation and human error. Therefore, a computer-aided diagnosis system is introduced as a solution to the problem. One of the diagnostic systems that has been built is NeuralPap. However, the NeuralPap performance is limited by several constraints. This research proposed several new image processing algorithms to reduce these constraints. The Adaptive Fuzzy-k-Means (AFKM) clustering algorithm is proposed to replace the Moving k-Means (MKM) to segment Pap smear images into the nucleus, cytoplasm and background regions. Next, the feature extraction algorithm based on pseudo colouring called the Pseudo Colour Feature Extraction (PCFE) manual and Semi-Automatic PCFE are designed to replace the Region Growing Based Feature Extraction (RGBFE) which uses monochromatic images. This research is a step forward compared with the NeuralPap system by proposing the feature extraction algorithm for overlapping cells by combining the concept of colour space with Semi-Automatic PCFE algorithm. In addition, this research has also suggested the AFKM algorithm as a new centre positioning algorithm for the Radial Basis Function (RBF) and Hybrid RBF (HRBF) networks replacing the MKM algorithm. The entire proposed algorithm has been proven to produce better performance than the corresponding algorithm used in the NeuralPap. In addition, the combination of all algorithms has managed to increase the accuracy of the classification of cervical cancer to 76.35%, compared with 73.40% which is obtained from the previous NeuralPap system.