Automated Counting of Cells in Breast Cytology Images Using Level Set Method
Automated Counting of Cells in Breast Cytology Images Using Level Set Method
复制标题
使用水平集方法自动计数乳腺细胞学图像中的细胞
DOI：
10.1109/hpcc/smartcity/dss.2018.00258
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
A. Din
中科院分区：
文献类型：
--
作者：
S. Khan;N. Islam;Z. Jan;Hameed Ullah Shah;A. Din
对乳腺细胞学图像中的细胞进行统计分析，对于发达国家和发展中国家女性人群中各种疾病的诊断非常重要。对于病理学家来说，人工实时检测和计数癌细胞不仅困难，而且非常耗时。本文提出了一种基于细针吸取细胞学(FNAC)图像的乳腺细胞学自动分析算法。该技术使用包括感知信息(如颜色)和形态特征的统计方法来估计初始细胞边界。同样，水平集技术被用于高效和准确地识别细胞对象，这有助于对单个癌细胞乳腺细胞学图像进行精确计数。在所提出的方法的演示过程中获得的实验结果显示，在精度上与病理学家的人工计数具有很高的相关性。实践证明，该方法能有效地对癌细胞进行高精度的细胞计数，避免了病理学家手工计数的误差(如颜色变化、人为错误等)。
Statistical analysis of cells in breast cytology images is very important for the diagnosis of various diseases in the female population in developed and developing countries. Manual detection and counting of the cancer cell in real time is not only difficult but hugely time-consuming for pathologists. In this paper, we propose an algorithm for automatic analysis of breast cytology using Fine Needle Aspiration Cytology (FNAC) images. The proposed technique uses statistical measures which include perceptual information (like color) and morphological characteristics for the estimation of the initial cell boundary. Similarly, the level set technique is used for efficient and accurate identification of cellular objects which help in the precise counting of individual cancer cell breast cytology images. Experimental results obtained during the demonstration of the proposed approach show high correlations in precision with manual counting by a pathologist. It has been proved that the proposed approach is efficient in processing cells for counting the cancerous cells with high accuracy and avoid discrepancies(like color variations, human error) in manual counting by a pathologist.