ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2012
Window classification of brain CT images in biomedical articles.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Intracranial hemorrhage segmentation and classification framework in computer tomography images using deep learning techniques.Scientific reports · 2025Article
- Radiopacity Enhancements in Polymeric Implant Biomaterials: A Comprehensive Literature Review.ACS biomaterials science & engineering · 2024Review
- Article
- Intracranial Hemorrhage Detection Using Parallel Deep Convolutional Models and Boosting Mechanism.Diagnostics (Basel, Switzerland) · 2023Article
- Creation of an anthropomorphic CT head phantom for verification of image segmentation.Medical physics · 2020Article
- Measurements in cervical vertebrae CT of pediatric cases: normal values.Japanese journal of radiology · 2018Article
- Cranial CT scan evaluation of morphological variations and location of pterion in Pakistani male population for lateral neurosurgical approach.Pakistan journal of medical sciencesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Effective capability to search biomedical articles based on visual properties of article images may significantly augment information retrieval in the future. In this paper, we present a new method to classify the window setting types of brain CT images. Windowing is a technique frequently used in the evaluation of CT scans, and is used to enhance contrast for the particular tissue or abnormality type being evaluated. In particular, it provides radiologists with an enhanced view of certain types of cranial abnormalities, such as the skull lesions and bone dysplasia which are usually examined using the " bone window" setting and illustrated in biomedical articles using "bone window images". Due to the inherent large variations of images among articles, it is important that the proposed method is robust. Our algorithm attained 90% accuracy in classifying images as bone window or non-bone window in a 210 image data set.
Indexed as
Identifiers
23304378PMC3540547What OpenQuestion holds
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.