A Fast Fuzzy-C means based marker controlled watershed segmentation of clustered nuclei

Microscopy cell image analysis is a fundamental tool for biological research. This analysis is used in studies of different aspects of cell cultures. The main challenges in segmenting nuclei in histometry are due to the fact that the specimen is a 2-D section of a 3-D tissue sample. The 2-D sectioning can result in partially imaged nuclei, sectioning of nuclei at odd angles, and damage due to the sectioning process. The classic methodology for cell detection is image segmentation, which is a fundamental and difficult problem in computer vision. The difficulty in automatic segmentation of images of cells is often uneven due to auto fluorescence from the tissue and fluorescence from out-of-focus objects. This unevenness makes the separation of foreground and background a non-trivial task. The intensity variations within the nuclei further complicate the segmentation as the nuclei may be split into more than one object, leading to over-segmentation. Due to the cell nuclei are often clustered, make it difficult to separate the individual nuclei. Hence an automatic segmentation of cell nuclei is an essential step in image histometry and cytometry. This paper presents a very fast method to segment clustered nuclei cells by performing initial segmentation using FCM, then shape markers and marking function in a watershed-like algorithm are used to accurately separate clustered nuclei. The proposed approach gives a good tradeoff between the execution time, easy usability, and efficiency and segmentation quality. The experimental results demonstrate the effectiveness of the proposed approach.