Image similarity‐based cardiac rhythm device identification from X‐rays using feature point matching.
Aims: Identifying the manufacturer and the type of cardiac implantable electronic devices (CIEDs) is important in emergent clinical settings. Recent studies have illustrated that artificial neural network models can successfully recognize CIEDs from chest X‐ray images. However, all existing methods...
| Publicado en: | Pacing & Clinical Electrophysiology Vol. 44; no. 4; pp. 633 - 641 |
|---|---|
| Autores principales: | , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Apr2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149757166&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149757166 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01478389 4F8 jtl: Pacing & Clinical Electrophysiology issn: 01478389 maglogo: Y pubinfo: dt: Apr2021 vid: 44 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 149757166 149263453 149757166 149757166 10.1111/pace.14209 149757166 ppf: 633 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Image similarity‐based cardiac rhythm device identification from X‐rays using feature point matching. aug: au: Higaki, Akinori Kurokawa, Tsukasa Kazatani, Takuro Kido, Shinsuke Aono, Tetsuya Matsuda, Kensho Tanaka, Yuta Kosaki, Tetsuya Kawamura, Go Shigematsu, Tatsuya Kawada, Yoshitaka Hiasa, Go Yamada, Tadakatsu Okayama, Hideki affil: Department of Cardiology, Ehime Prefectural Central Hospital, Matsuyama Ehime, , Japan sug: subj: Defibrillators, Implantable Pacemaker, Artificial Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Methods Image Processing, Computer Assisted Human Descriptive Statistics Algorithms ab: Aims: Identifying the manufacturer and the type of cardiac implantable electronic devices (CIEDs) is important in emergent clinical settings. Recent studies have illustrated that artificial neural network models can successfully recognize CIEDs from chest X‐ray images. However, all existing methods require a vast amount of medical data to train the classification model. Here, we have proposed a novel method to retrieve an identical CIED image from an image database by employing the feature point matching algorithm. Methods and results: A total of 653 unique X‐ray images from 456 patients who visited our pacemaker clinic between April 2012 and August 2020 were collected. The device images were manually square‐shaped, and was thereafter resized to 224 × 224 pixels. A scale‐invariant feature transform (SIFT) algorithm was used to extract the keypoints from the query image and reference images. Paired feature points were selected via brute‐force matching, and the average Euclidean distance was calculated. The image with the shortest average distance was defined as the most similar image. The classification performance was indicated by accuracy, precision, recall, and F1‐score for detecting the manufacturers and model groups, respectively. The average accuracy, precision, recall, and F‐1 score for the manufacturer classification were 97.0%, 0.97, 0.96, and 0.96, respectively. For the model classification task, the average accuracy, precision, recall, and F‐1 score were 93.2%, 0.94, 0.92, and 0.93, respectively, all of which were higher than those of the previously reported machine learning models. Conclusion: Feature point matching is useful for identifying CIEDs from X‐ray images. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|