A novel approach for detection and delineation of cell nuclei using feature similarity index measure

Accurate image segmentation of cells and tissues is a challenging research area due to its vast applications in medical diagnosis. Seed detection is the basic and most essential step for the automated segmentation of microscopic images. This paper presents a robust, accurate and novel method for detecting cell nuclei which can be efficiently used for cell segmentation. We propose a template matching method using a feature similarity index measure (FSIM) for detecting nuclei positions in the image which can be further used as seeds for segmentation tasks. Initially, a Fuzzy C-Means clustering algorithm is applied on the image for separating the foreground region containing the individual and clustered nuclei regions. FSIM based template matching approach is then used for nuclei detection. FSIM makes use of low level texture features for comparisons and hence gives good results. The performance of the proposed method is evaluated on the gold standard dataset containing 36 images (∼8000 nuclei) of tissue samples and also in vitro cultured cell images of Stromal Fibroblasts (5 images) and Human Macrophage cell line (4 images) using the statistical measures of Precision and Recall. The results are analyzed and compared with other state-of-the-art methods in the literature and software tools to prove its efficiency. Precision is found to be comparable and the Recall rate is found to exceed 92% for the gold standard dataset which shows considerable performance improvement over existing methods.