Publication:
Nuclei detection in hepatocellular carcinoma and dysplastic liver nodules in histopathology images using bootstrap regression

Simple item page
dc.contributor.authorKalinathan, Lekshmi
dc.contributor.authorKathavarayan, Ruba Soundar
dc.contributor.authorKanmani, Madheswari
dc.contributor.authorDinakaran, Nagendram
dc.date.accessioned2022-12-09T11:45:50Z
dc.date.available2022-12-09T11:45:50Z
dc.date.issued2020
dc.description.abstractHepatocellular 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 nucleies
dc.formatapplication/pdfes
dc.format.extent9es
dc.identifier.citationHistology and Histopathology Vol. 35, nº10 (2020)
dc.identifier.doihttps://doi.org/10.14670/HH-18-240
dc.identifier.issn0213-3911
dc.identifier.issn1699-5848
dc.identifier.urihttp://hdl.handle.net/10201/126285
dc.languageenges
dc.publisherUniversidad de Murcia, Departamento de Biologia Celular e Histiologiaes
dc.relationSin financiación externa a la Universidades
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectHistopatholgyes
dc.subjectDysplasiaes
dc.subjectHepatocellular Carcinomaes
dc.subjectHepatic tumores
dc.subjectClassificationes
dc.subject.otherCDU::6 - Ciencias aplicadas::61 - Medicina::616 - Patología. Medicina clínica. Oncologíaes
dc.titleNuclei detection in hepatocellular carcinoma and dysplastic liver nodules in histopathology images using bootstrap regressiones
dc.typeinfo:eu-repo/semantics/articlees
dspace.entity.typePublicationes
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Kalinathan-35-1115-1123-2020.pdf
Size:
6.47 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.39 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Histology and histopathology Vol.35,nº10 (2020)