ArticleAnalytical cellular pathology (Amsterdam)2017
Texture Analysis of Abnormal Cell Images for Predicting the Continuum of Colorectal Cancer.
Article in Analytical cellular pathology (Amsterdam), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 22 citations in OpenAlex.
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- Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey.Journal of pathology informatics · 2023Review
- Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images.Nature communications · 2021Article
- Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence.BMC medicine · 2021Article
- A Novel Machine Learning-derived Radiomic Signature of the Whole Lung Differentiates Stable From Progressive COVID-19 Infection: A Retrospective Cohort Study.Journal of thoracic imaging · 2020Article
- Segmentation and Grade Prediction of Colon Cancer Digital Pathology Images Across Multiple Institutions.Cancers · 2019Article
- On Structural Entropy and Spatial Filling Factor Analysis of Colonoscopy Pictures.Entropy (Basel, Switzerland) · 2019Article
- [Prediction of the therapeutic response after target-combined chemotherapy treatment for patients with liver metastasis from colorectal cancer using computed tomography texture analysis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2018Article
- Radiomics Evaluation of Histological Heterogeneity Using Multiscale Textures Derived From 3D Wavelet Transformation of Multispectral Images.Frontiers in oncology · 2018Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Abnormal cell (ABC) is a markedly heterogeneous tissue area and can be categorized into three main types: benign hyperplasia (BH), carcinoma (Ca), and intraepithelial neoplasia (IN) or precursor cancerous lesion. In this study, the goal is to determine and characterize the continuum of colorectal cancer by using a 3D-texture approach. ABC was segmented in preprocessing step using an active contour segmentation technique. Cell types were analyzed based on textural features extracted from the gray level cooccurrence matrices (GLCMs). Significant texture features were selected using an analysis of variance (ANOVA) of ABC with a
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