Por favor, use este identificador para citar o enlazar este ítem:
https://doi.org/10.14670/HH-18-240
Twittear
Título: | Nuclei detection in hepatocellular carcinoma and dysplastic liver nodules in histopathology images using bootstrap regression |
Fecha de publicación: | 2020 |
Editorial: | Universidad de Murcia, Departamento de Biologia Celular e Histiologia |
Cita bibliográfica: | Histology and Histopathology Vol. 35, nº10 (2020) |
ISSN: | 0213-3911 1699-5848 |
Materias relacionadas: | CDU::6 - Ciencias aplicadas::61 - Medicina::616 - Patología. Medicina clínica. Oncología |
Palabras clave: | Histopatholgy Dysplasia Hepatocellular Carcinoma Hepatic tumor Classification |
Resumen: | Hepatocellular carcinoma (HCC) is the most common primary malignant neoplasm of the liver representing the fifth most common malignancy worldwide. This tumor is more common in men than women, with a ratio of 2.7:1. Unlike HCC, Dysplasia is the precancerous nature of liver nodules and is characterized by cellular and nuclear enlargement, nuclear pleomorphism, and multinucleation. Area based Adaptive Expectation Maximization (EM) uses texture, layout, and context features of cells, and grows clusters to obtain texton maps of nucleus. A discriminative model of nucleus and cytoplastic changes of tumor is built by incorporating texture, layout, and context information efficiently. A bootsrap regression model of nuclei and cytoplastic changes are built by incorporating the aforementioned features efficiently. Mean squared error, Peak Signal to Noise ratio and Dice similarity values are used to evaluate the method's classification performance. The proposed method provides high classification and segmentation accuracy of nucleus and extra nuclear content in HCC and dysplasia, which are exceedingly textured in histopathology images, when compared to Adaptive K means, EM method and the state-of-the-art method, Convolutional Neural Networks (CNN). As texton detection reduces the cluttered background of nuclei, the proposed method would be a convenient mechanism for the classification of nuclei and non- nuclear features. In conclusion, this system can detect more eligible cells of precancerous nature as well as malignant cells even in a cluttered background of nuclei |
Autor/es principal/es: | Kalinathan, Lekshmi Kathavarayan, Ruba Soundar Kanmani, Madheswari Dinakaran, Nagendram |
URI: | http://hdl.handle.net/10201/126285 |
DOI: | https://doi.org/10.14670/HH-18-240 |
Tipo de documento: | info:eu-repo/semantics/article |
Número páginas / Extensión: | 9 |
Derechos: | info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
Aparece en las colecciones: | Vol.35,nº10 (2020) |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
Kalinathan-35-1115-1123-2020.pdf | 6,63 MB | Adobe PDF | Visualizar/Abrir |
Este ítem está sujeto a una licencia Creative Commons Licencia Creative Commons