SRM Superpixel Merging Framework for Precise Segmentation of Cervical Nucleus

Abstract

Cervical nuclei contain important diagnostic characteristics useful for identifying abnormality in cervical cells. Therefore, an accurate segmentation of nuclei is the primary step in computer-aided diagnosis. However, cell overlapping, uneven staining, poor contrast, and presence of debris elements make this task challenging. A novel method is presented in this paper to detect and segment nuclei from overlapping cervical smear images. The proposed framework segments nuclei by merging superpixels generated by statistical region merging (SRM) algorithm using pairwise regional contrasts and gradient boundaries. To overcome the limitation of finding the optimal parameter value, which controls the coarseness of the segmentation, a new approach for SRM superpixel generation was introduced. Quantitative and qualitative assessment of the proposed framework is carried out using Overlapping Cervical Cytology Image Segmentation Challenge — ISBI 2014 dataset of 945 cervical images. In comparison with the state-of-the-art methods, the proposed methodology achieved superior segmentation performance in terms of Dice similarity coefficient 0.956 and pixel-based recall 0.962. Other evaluation measures such as pixel-based precision 0.930, object-based precision 0.987, and recall 0.944, also compare favorably with some recently published studies. The experimental results demonstrate that the proposed framework can precisely segment nuclei from overlapping cervical cell images, while keeping high level of precision and recall. Therefore, the developed framework may assist cytologists in computerized cervical cell analysis and help with early diagnosis of cervical cancer.