ReviewComputational and structural biotechnology journal2017
Mining textural knowledge in biological images: Applications, methods and trends.
Review in Computational and structural biotechnology journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
23 citing papers in PubMed, 70 citations in OpenAlex.
- Data-driven image analysis to determine antibody-induced dissociation of cell-cell adhesion and antibody pathogenicity in pemphigus.PNAS nexus · 2025Article
- Multiscale chromatin dynamics and high entropy in plant iPSC ancestors.Journal of cell science · 2024Article
- A comprehensive AI model development framework for consistent Gleason grading.Communications medicine · 2024Article
- Protein feature engineering framework for AMPylation site prediction.Scientific reports · 2024Article
- Integrating imaging-based classification and transcriptomics for quality assessment of human oocytes according to their reproductive efficiency.Journal of assisted reproduction and genetics · 2023Article
- Prediction of lymph node status in patients with early-stage cervical cancer based on radiomic features of magnetic resonance imaging (MRI) images.BMC medical imaging · 2023Article
- Dual Deep CNN for Tumor Brain Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Influence of maternal psychological distress during COVID-19 pandemic on placental morphometry and texture.Scientific reports · 2023Article
- Disorganization and Musculoskeletal Diseases: Novel Insights into the Enigma of Unexplained Bone Abnormalities and Fragility Fractures.Current osteoporosis reports · 2023Review
- Prediction of mechanistic subtypes of Parkinson's using patient-derived stem cell models.Nature machine intelligence · 2023Article
- Comprehensive analysis of prediction of the EGFR mutation and subtypes based on the spinal metastasis from primary lung adenocarcinoma.Frontiers in oncology · 2023Article
- Texture Analysis in Brain Tumor MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Article
- Evaluation of Bone Marrow Texture and Trabecular Changes With Quantitative DCE-MRI and QCT in Alloxan-Induced Diabetic Rabbit Models.Frontiers in endocrinology · 2021Article
- Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images.BioMed research international · 2021Article
- Shifts in the developmental rate of spadefoot toad larvae cause decreased complexity of post-metamorphic pigmentation patterns.Scientific reports · 2020Article
- Rheological and Antimicrobial Properties of Chitosan and Quinoa Protein Filmogenic Suspensions with Thyme and Rosemary Essential Oils.Foods (Basel, Switzerland) · 2020Article
- Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient's Pathological Grades.Frontiers in oncology · 2020Article
- Classification and Diagnosis of Thyroid Carcinoma Using Reinforcement Residual Network with Visual Attention Mechanisms in Ultrasound Images.Journal of medical systems · 2019Article
- Texture Analysis in Cerebral Gliomas: A Review of the Literature.AJNR. American journal of neuroradiology · 2019Review
- A Deep Learning Approach to the Screening of Oncogenic Gene Fusions in Humans.International journal of molecular sciences · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Texture analysis is a major task in many areas of computer vision and pattern recognition, including biological imaging. Indeed, visual textures can be exploited to distinguish specific tissues or cells in a biological sample, to highlight chemical reactions between molecules, as well as to detect subcellular patterns that can be evidence of certain pathologies. This makes automated texture analysis fundamental in many applications of biomedicine, such as the accurate detection and grading of multiple types of cancer, the differential diagnosis of autoimmune diseases, or the study of physiological processes. Due to their specific characteristics and challenges, the design of texture analysis systems for biological images has attracted ever-growing attention in the last few years. In this paper, we perform a critical review of this important topic. First, we provide a general definition of texture analysis and discuss its role in the context of bioimaging, with examples of applications from the recent literature. Then, we review the main approaches to automated texture analysis, with special attention to the methods of feature extraction and encoding that can be successfully applied to microscopy images of cells or tissues. Our aim is to provide an overview of the state of the art, as well as a glimpse into the latest and future trends of research in this area.
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Registered trials
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.