Performance Evaluation of State-of-the-Art Local Feature Detectors and Descriptors in the Context of Longitudinal Registration of Retinal Images.
In this paper we systematically evaluate the performance of several state-of-the-art local feature detectors and descriptors in the context of longitudinal registration of retinal images. Longitudinal (temporal) registration facilitates to track the changes in the retina that has happened over time....
| Publicado en: | Journal of Medical Systems Vol. 42; no. 4; pp. 1 - 2 |
|---|---|
| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Apr2018
|
| 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=128680934&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128680934 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Apr2018 vid: 42 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 128680934 128680934 128680934 10.1007/s10916-018-0911-z 128680934 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Performance Evaluation of State-of-the-Art Local Feature Detectors and Descriptors in the Context of Longitudinal Registration of Retinal Images. aug: au: Saha, Sajib K. Xiao, Di Frost, Shaun Kanagasingam, Yogesan affil: Australian E Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Perth, Australia sug: subj: Retina Analysis Image Processing, Computer Assisted Algorithms Evaluation Descriptive Statistics Comparative Studies Artificial Intelligence Reliability Human ab: In this paper we systematically evaluate the performance of several state-of-the-art local feature detectors and descriptors in the context of longitudinal registration of retinal images. Longitudinal (temporal) registration facilitates to track the changes in the retina that has happened over time. A wide number of local feature detectors and descriptors exist and many of them have already applied for retinal image registration, however, no comparative evaluation has been made so far to analyse their respective performance. In this manuscript we evaluate the performance of the widely known and commonly used detectors such as Harris, SIFT, SURF, BRISK, and bifurcation and cross-over points. As of descriptors SIFT, SURF, ALOHA, BRIEF, BRISK and PIIFD are used. Longitudinal retinal image datasets containing a total of 244 images are used for the experiment. The evaluation reveals some potential findings including more robustness of SURF and SIFT keypoints than the commonly used bifurcation and cross-over points, when detected on the vessels. SIFT keypoints can be detected with a reliability of 59% for without pathology images and 45% for with pathology images. For SURF keypoints these values are respectively 58% and 47%. ALOHA descriptor is best suited to describe SURF keypoints, which ensures an overall matching accuracy, distinguishability of 83%, 93% and 78%, 83% for without pathology and with pathology images respectively. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|