Towards rapid cervical cancer diagnosis: automated detection and classification of pathologic cells in phase-contrast images

Abstract

In this paper, a combination of two methods based on texture analysis, contour grouping, and pattern recognition techniques is presented to detect and classify pathologic cells in cervical vaginal smears using the phase-contrast microscopy. The first method applies statistical geometrical features to detect image regions that contain epithelial cells and hide those regions with medium and contamination. Sequential forward floating selection was used to identify the most representative features. A shape of cells was identified by applying an active contour model supported by some postprocessing techniques. The second method applies edge detection, ridge following, contour grouping, and Fisher linear discriminant to detect abnormal nuclei. Evaluation of the algorithms' performance and comparison with alternative approaches show that both methods are reliable and, when combined, improve the classification. By presenting only images or their parts that are diagnostically important, the method unburdens a physician from massive and messy data. It also indicates abnormalities marking atypical nuclei and, in that sense, supports diagnosis of cervical cancer.

Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System

“…The combined applications of the image processing technique and Artificial Intelligence have also gained considerable interest among previous scholars to form a comprehensive diagnosis system [13][14][15][16]. In most research, the image processing technique serves as a pre-processing tool for the purpose of improving the image quality and further to make the data acquisition process easier.…”

Image Texture Characterization Using the Discrete Orthonormal S-Transform

“…Various mathematical techniques to quantify image texture, including statistical, Fourier, and wavelet-based methods, have been applied to radiological images of numerous pathologies, such as multiple sclerosis4,5, brain tumors6, liver diseases7, infarcted myocardial tissue8, and normal tissues of the knee9, and has even been used for automated detection and classification based on phase-contrast microscopy images, such as those used in cervical cancer diagnosis10.…”

MRI Texture Analysis Predicts p53 Status in Head and Neck Squamous Cell Carcinoma

“…[15][16][17][18] In addition to anatomic details, the analysis of MR images provides additional metabolic and biologic information in tumors. 19 Mathematic techniques that quantify image characteristics have been applied to a vast array of pathologies, from multiple sclerosis, 20 attention deficit/hyperactivity disorder, 21 and Alzheimer disease 22 to breast cancer, 23 cervical cancer, 24 and brain tumors. 25 Studies in glioblastoma have shown that there is a correlation between the methylation of O6-methylguanine-DNA methyltransferase and MR imaging features.…”