ArticleIEEE journal of translational engineering in health and medicine2018
Histogram-Based Features Selection and Volume of Interest Ranking for Brain PET Image Classification.
Article in IEEE journal of translational engineering in health and medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes.Annual review of biomedical data science · 2024Review
- Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers.Cancers · 2022Review
- Neurodegenerative diseases-Caps: a capsule network based early screening system for the classification of neurodegenerative diseases.Cognitive neurodynamics · 2022Article
- Alzheimer's Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI.Frontiers in aging neuroscience · 2022Article
- Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract.Brain communications · 2022Article
- Comparing Methods of Feature Extraction of Brain Activities for Octave Illusion Classification Using Machine Learning.Sensors (Basel, Switzerland) · 2021Article
- Computer-Aided Diagnosis System for Alzheimer's Disease Using Positron Emission Tomography Images.Interdisciplinary sciences, computational life sciences · 2021Article
- Artificial intelligence for molecular neuroimaging.Annals of translational medicine · 2021Review
- PET Imaging of Tau Pathology and Amyloid-β, and MRI for Alzheimer's Disease Feature Fusion and Multimodal Classification.Journal of Alzheimer's disease : JAD · 2021Article
- Machine Learning Based Automated Segmentation and Hybrid Feature Analysis for Diabetic Retinopathy Classification Using Fundus Image.Entropy (Basel, Switzerland) · 2020Article
- Machine learning studies on major brain diseases: 5-year trends of 2014-2018.Japanese journal of radiology · 2019Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Positron emission tomography (PET) is a molecular medical imaging modality which is commonly used for neurodegenerative diseases diagnosis. Computer-aided diagnosis, based on medical image analysis, could help quantitative evaluation of brain diseases such as Alzheimer's disease (AD). A novel method of ranking the effectiveness of brain volume of interest (VOI) to separate healthy control from AD brains PET images is presented in this paper. Brain images are first mapped into anatomical VOIs using an atlas. Histogram-based features are then extracted and used to select and rank VOIs according to the area under curve (AUC) parameter, which produces a hierarchy of the ability of VOIs to separate between groups of subjects. The top-ranked VOIs are then input into a support vector machine classifier. The developed method is evaluated on a local database image and compared to the known selection feature methods. Results show that using AUC outperforms classification results in the case of a two group separation.
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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.