… of cells for an efficient segmentation. … for histopathological images. Then, the k-means
clustering algorithm is basically applied to segment cellular and non-cellular structures in an image…

… were selected as candidates to represent difficult-to-detect images due to their relatively
huge number of cancer cells. Fig 7 illustrates the results of our algorithm when applied to a …

… 4b, we show an example of the Otsu algorithm applied to a histopathological image. … efficient
method, FECS, for cell nuclei segmentation for histopathological images. We validated our …

… of the mitotic cells. We propose a fast and accurate approach for automatic mitosis detection
… histopathological images. We employ area morphological scale space for cell segmentation…

… We also compare with nuclei segmentation toolboxes available in Cell Profiler [81] and
ImageJ-Fiji [80]. These comparisons and representative patches from different organs are shown …

… best segments the cell nuclei of the histopathological images and a Bayesian classifier was
used for meningioma subtype discrimination. Overall classification accuracy of 92.50% was …

… two types of tissues in histological images. Automated segmentation or classification of EP
and … graph feature describing the topological distribution of the tissue cell nuclei was used for …

… Based on the proposed cell-level visual representation learning… varieties of cellular elements
to perform histopathology image … cell-level images as the output from nuclei segmentation. …

… Therefore, DL-based approaches are now a gold standard for different colon cancer
related applications namely gland segmentation, tumour micro-environment and cells …

… Manual segmentation … histopathological datasets report poor performance on other datasets
due again to the high variability in acquisition parameters and biological properties of cells …