Cervical cancer is one of the most deadly and common forms of cancer among women if no
action is taken to prevent it, yet it is preventable through a simple screening test, the so …

This paper presents an overview of the image analysis techniques in the domain of
histopathology, specifically, for the objective of automated carcinoma detection and …

Cervical cancer is the fourth most prevalent disease in women. Accurate and timely cancer
detection can save lives. Automatic and reliable cervical cancer detection methods can be …

Cervical cancer has been one of the most lethal cancers threatening women's health.
Nevertheless, the incidence of cervical cancer can be effectively minimized with preventive …

Cytology is a branch of pathology that deals with the microscopic examination of cells for
diagnosis of carcinoma or inflammatory conditions. In the present work, the term cytology is …

In order to automate cervical cancer screening tests, one of the most important and
longstanding challenges is the segmentation of cell nuclei in the stained specimens. Though …

Aims: To train a convolutional neural network (CNN) to identify abnormal foci from LBCC
smears. Settings and Design: We have chosen retrospective study design from archived …

In a full automatic cervical cytology screening process, one of the essential steps is the
segmentation of cervical nuclei. Despite some progress, there is a need to improve …

Segmentation of cell nuclei in PAP-smear cervical images is of preeminent importance in
computer-aided-diagnostic screening technique for cervical cancer. This paper proposes a …

In this paper we present a multiphase level set model for histology image segmentation.
Global K-means energy is weighted by a Gaussian kernel to cluster image pixels in local …