ArticlePloS one2016
Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Generalized deep learning for histopathology image classification using supervised contrastive learning.Journal of advanced research · 2025Article
- Texture analysis of CT colonography to develop a novel imaging biomarker for the management of colorectal cancer.Annals of gastroenterological surgery · 2025Article
- Prediction of lymphovascular invasion in esophageal squamous cell carcinoma by computed tomography-based radiomics analysis: 2D or 3D ?Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- SnapEnsemFS: a snapshot ensembling-based deep feature selection model for colorectal cancer histological analysis.Scientific reports · 2023Article
- Rapid Polyp Classification in Colonoscopy Using Textural and Convolutional Features.Healthcare (Basel, Switzerland) · 2022Article
- Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review.Cancers · 2021Review
- Hyperspectral and multispectral imaging in digital and computational pathology: a systematic review [Invited].Biomedical optics express · 2020Review
- Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer.Cancer research and treatment · 2020Article
- Magnetic-Resonance-Imaging Texture Analysis Predicts Early Progression in Rectal Cancer Patients Undergoing Neoadjuvant Chemoradiation.Gastroenterology research and practice · 2019Article
- Prediction of survival with multi-scale radiomic analysis in glioblastoma patients.Medical & biological engineering & computing · 2018Article
- MRI features predict p53 status in lower-grade gliomas via a machine-learning approach.NeuroImage. Clinical · 2018Article
- Predicting Gleason Score of Prostate Cancer Patients Using Radiomic Analysis.Frontiers in oncology · 2018Article
- Using spectral imaging for the analysis of abnormalities for colorectal cancer: When is it helpful?PloS one · 2018Article
- Radiomics Evaluation of Histological Heterogeneity Using Multiscale Textures Derived From 3D Wavelet Transformation of Multispectral Images.Frontiers in oncology · 2018Article
- Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age.Scientific reports · 2017Article
- Classifications of Multispectral Colorectal Cancer Tissues Using Convolution Neural Network.Journal of pathology informatics · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma. MATERIALS AND
methodsIn the proposed approach, the region of interest containing PT is first extracted from multispectral images using active contour segmentation. This region is then encoded using texture features based on the Laplacian-of-Gaussian (LoG) filter, discrete wavelets (DW) and gray level co-occurrence matrices (GLCM). To assess the significance of textural differences between PT types, a statistical analysis based on the Kruskal-Wallis test is performed. The usefulness of texture features is then evaluated quantitatively in terms of their ability to predict PT types using various classifier models.
resultsPreliminary results show significant texture differences between PT types, for all texture features (p-value < 0.01). Individually, GLCM texture features outperform LoG and DW features in terms of PT type prediction. However, a higher performance can be achieved by combining all texture features, resulting in a mean classification accuracy of 98.92%, sensitivity of 98.12%, and specificity of 99.67%.
conclusionsThese results demonstrate the efficiency and effectiveness of combining multiple texture features for characterizing the continuum of CRC and discriminating between pathological tissues in multispectral images.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.