Liliana Salazar

An Automatic Segmentation Approach of Epithelial Cells Nuclei

Histology images are used to identify biological structures present in living organisms — cells, tissues, organs, and parts of organs. E-Learning systems can use images to aid teaching how morphological features relate to function and understanding which features are most diagnostic of organs. The structure of cells varies according to the type and function of the cell. Automatic cell segmentation is one of the challenging tasks in histology image processing. This problem has been addressed using morphological gradient, region-based methods and shape-based method approaches, among others. In this paper, automatic segmentation of nuclei of epithelial cells is addressed by including morphological information. Image segmentation is commonly evaluated in isolation. This is either done by observing results, via manual segmentation or via some other goodness measure that does not rely on ground truth images. Expert criteria along with images manually segmented are used to validate automatic segmentation results. Experimental results show that the proposed approach segments epithelial cells in a close way to expert manual segmentations. An average sensitivity of 76% and an average specificity of 77% were obtained on a selected set of images.

Automatic Classification of Coating Epithelial Tissue

Histology images may be used in E-Learning systems to teach how morphological features and function of each organ contribute to its identification. Automatic classification of coating epithelial cells is an open problem in image processing. This problem has been addressed using morphological gradient, region-based and, shape-based method, among others. In this paper, coating epithelial cells are recognised and classified into: Flat, Cubic and Cylindrical. Epithelial cells are classified based on sphericity and projection. Information about sphericity is used to classify cells into cubic and a measure based in projecting cell nucleus into light region is used to classify into flat and cylindrical. Experimental validations are conducted according to expert criteria, along with manually annotated images, as a ground-truth. Experimental results revealed that the proposed approach recognised coating epithelial cells and classified tissues in a similar way to how experts have performed these classifications.

Identifying Loose Connective and Muscle Tissues on Histology Images

Histology images are used to identify biological structures present in living organisms — cells, tissues, and organs — correctly. The structure of tissues varies according to the type and purpose of the tissue. Automatic identification of tissues is an open problem in image processing. In this paper, the identification of loose connective and muscle tissues based on morphological tissue information is presented.

Automatic recognition of fundamental tissues on histology images of the human cardiovascular system

Cardiovascular disease is the leading cause of death worldwide. Therefore, techniques for improving diagnosis and treatment in this field have become key areas for research. In particular, approaches for tissue image processing may support education system and medical practice. In this paper, an approach to automatic recognition and classification of fundamental tissues, using morphological information is presented. Taking a 40× or 10× histological image as input, three clusters are created with the k-means algorithm using a structural tensor and the red and the green channels. Loose connective tissue, light regions and cell nuclei are recognised on 40× images. Then, the cell nucleis features - shape and spatial projection - and light regions are used to recognise and classify epithelial cells and tissue into flat, cubic and cylindrical. In a similar way, light regions, loose connective and muscle tissues are recognised on 10× images. Finally, the tissues function and composition are used to refine muscle tissue recognition. Experimental validation is then carried out by histologist following expert criteria, along with manually annotated images that are used as a ground-truth. The results revealed that the proposed approach classified the fundamental tissues in a similar way to the conventional method employed by histologists. The proposed automatic recognition approach provides for epithelial tissues a sensitivity of 0.79 for cubic, 0.85 for cylindrical and 0.91 for flat. Furthermore, the experts gave our method an average score of 4.85 out of 5 in the recognition of loose connective tissue and 4.82 out of 5 for muscle tissue recognition.

A histological ontology of the human cardiovascular system

AbstractBackgroundIn this paper, we describe a histological ontology of the human cardiovascular system developed in collaboration among histology experts and computer scientists.ResultsThe histological ontology is developed following an existing methodology using Conceptual Models (CMs) and validated using OOPS!, expert evaluation with CMs, and how accurately the ontology can answer the Competency Questions (CQ). It is publicly available at http://bioportal.bioontology.org/ontologies/HO and https://w3id.org/def/System.ConclusionsThe histological ontology is developed to support complex tasks, such as supporting teaching activities, medical practices, and bio-medical research or having natural language interactions.

Anisakidae nematodes isolated from the flathead grey mullet fish (Mugil cephalus) of Buenaventura, Colombia

Anisakiasis is a parasitic infection caused by larval stages of nematodes of the genus Anisakis, Pseudoterranova and Contracaecum, of the Anisakidae family. The lifecycle of these nematodes develops in aquatic organisms and their final hosts are marine mammals. However, humans can act as accidental hosts and become infected with infective stage larvae (L3) by consuming raw or undercooked fish or shellfish carrying the parasite. Of this group of parasites, the genus Anisakis is the most studied: its presence in humans is associated with non-specific gastrointestinal symptoms or allergic responses that can trigger anaphylactic shock. The lack of studies in anisakiasis and Anisakis in Colombia has resulted in this infection being little-known by medical practitioners and therefore potentially underreported. The objective of this study was to identify anisakid nematodes in the flathead grey mullet fish (Mugil cephalus), caught by artisanal fishing methods and commercialized in Buenaventura. Morphological identification was carried out by classical taxonomy complemented by microscopy study using the histochemical technique Hematoxylin-Eosin. Nematodes of the genus Anisakis were found in the host M. cephalus. The Prevalence of Anisakis larvae in flathead grey mullet fish was 33%. The findings confirm the presence of Anisakis sp. in fish for human consumption in the Colombian Pacific region, a justification for further investigation into a possible emerging disease in this country.