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Automatic segmentation of cell nuclei using Krill Herd optimization based multi-thresholding and Localized Active Contour Model

Beevi K. S., Nair M. S., Bindu G. R.

Biocybernetics and Biomedical Engineering

2016

Vol. 36, no. 4

584--596

Analysis of tissue components in histopathology image stays on as the gold standard in detecting different types of cancers. Active Contour Models (ACM) serve as a widely useful tool in object segmentation in pathology images. Since the ACMs are susceptible to initial contour placement, efficiency of object detection is very much influenced by the selection of primary curve placement technique. In this paper, in order to handle diffused intensities present along object boundaries in histopathology images, segmentation of nuclei from breast histopathology images are carried out by Localized Active Contour Model (LACM) utilizing bio-inspired optimization techniques in the detection stage. Krill Herd Algorithm (KHA) based optimal curve placement provides better initial boundaries compared with other detection techniques. The segmentation performance is investigated based on Housdorff (HD) and Maximum Absolute Distance (MAD) measures. The algorithm also shows comparable performance with other state-of-the-art techniques in terms of quantitative measures such as Precision, Accuracy and Touching Nuclei Resolution when applied to complex images of stained breast biopsy slides.

Analysis of ramifications and carina shape in bronchoscopic images

Mikrut Z., Duplaga M.

Bio-Algorithms and Med-Systems

2010

Vol. 6, no. 12

15--20

Because of the growing volume of digital registrations obtained from bronchoscopic examinations it proved necessary to summarize (“brief”) their results, namely to mark the parts that are relevant and/or irrelevant for medical diagnosis. The parts in which the ramification areas of bronchial tree are visualized belong to the first group. The paper presents an automatic method for detection of the areas where next level orifices of bronchial tree are visible. The applied algorithms allow the shape evaluation for both orifices and the area located between them (carina). In both cases backpropagation type neural networks are used as classifiers.