Precise segmentation of Pap smear cell nucleus is crucial for early diagnosis of cervical cancer. This task is particularly challenging because of cell overlapping, inconsistent staining, poor contrast and other imaging artifacts. In this study, a novel method is proposed to segment cell nucleus from overlapping Pap smear cell images. The proposed technique introduces a circular shape function (CSF) to increase the robustness of Pap cell nucleus segmentation using fuzzy c-means clustering. CSF imposes a shape constrain over the formed clusters, while improves the boundary of the nucleus. The shape function helps to differentiate the pixels having similar intensity value but located in different spatial regions. The method is evaluated using Overlapping Cervical Cytology Image Segmentation Challenge - ISBI 2014 dataset and compared with the traditional FCM clustering and recently published state-of-the-art methods. Both qualitative and quantitative measures indicate that the new technique performs favorably with others.

Mass segmentation in mammograms is a challenging task if the mass is located in a local dense background. It can be due to the similarity of intensities between the overlapped normal dense breast tissue and mass. In this paper, a self- adjusted mammogram contrast enhancement solution called Adaptive Clip Limit CLAHE (ACL-CLAHE) is developed, aiming to improve mass segmentation in dense regions of mammograms. An optimization algorithm based on entropy is used to optimize the clip limit and window size of standard CLAHE. The proposed method is tested on 89 mammogram images with 41 masses localized in dense background and 48 masses in non-dense background. The results are compared with other standard enhancement techniques such as Adjustable Histogram Equalization, Unsharp Masking, Neutrosophy based enhancement, standard CLAHE and an Adaptive Clip Limit CLAHE based on standard deviation. The experimental results show that our method significantly improves the mass segmentation in local dense background without compromising the performance in local non-dense background.