Comparison of image registration methods for composing spectral retinal images

Laaksonen, Lauri; Claridge, Ela; Fält, Pauli; Hauta-Kasari, Markku; Uusitalo, Hannu; Lensu, Lasse

doi:10.1016/j.bspc.2017.03.003

Cited by 3 publications

(1 citation statement)

References 46 publications

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“…Such challenges have provided similar conclusions in previous studies that encompass a variety of applications. [27][28][29][30] One of the limitations of our studythe low number of points used for TRE calculations, was a result of these challenges. It took an experienced medical physicist about 20 min to mark the five points in each image.…”

Section: Discussionmentioning

confidence: 99%

Accuracy of deformable image registration techniques for alignment of longitudinal cholangiocarcinoma CT images

et al. 2020

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Purpose: Response assessment of radiotherapy for the treatment of intrahepatic cholangiocarcinoma (IHCC) across longitudinal images is challenging due to anatomical changes. Advanced deformable image registration (DIR) techniques are required to correlate corresponding tissues across time. In this study, the accuracy of five commercially available DIR algorithms in four treatment planning systems (TPS) was investigated for the registration of planning images with posttreatment follow-up images for response assessment or re-treatment purposes. Methods: Twenty-nine IHCC patients treated with hypofractionated radiotherapy and with pretreatment and posttreatment contrast-enhanced computed tomography (CT) images were analyzed. Liver segmentations were semiautomatically generated on all CTs and the posttreatment CT was then registered to the pretreatment CT using five commercially available algorithms (Demons, B-splines, salient feature-based, anatomically constrained and finite element-based) in four TPSs. This was followed by an in-depth analysis of 10 DIR strategies (plus global and liver-focused rigid registration) in one of the TPSs. Eight of the strategies were variants of the anatomically constrained DIR while the two were based on a finite element-based biomechanical registration. The anatomically constrained techniques were combinations of: (a) initializations with the two rigid registrations; (b) two similarity metricscorrelation coefficient (CC) and mutual information (MI); and (c) with and without a controlling region of interest (ROI)the liver. The finite element-based techniques were initialized by the two rigid registrations. The accuracy of each registration was evaluated using target registration error (TRE) based on identified vessel bifurcations. The results were statistically analyzed with a one-way analysis of variance (ANOVA) and pairwise comparison tests. Stratified analysis was conducted on the inter-TPS data (plus the liver-focused rigid registration) using treatment volume changes, slice thickness, time between scans, and abnormal lab values as stratifying factors. Results: The complex deformation observed following treatment resulted in average TRE exceeding the image voxel size for all techniques. For the inter-TPS comparison, the Demons algorithm had the lowest TRE, which was significantly superior to all the other algorithms. The respective mean (standard deviation) TRE (in mm) for the Demons, B-splines, salient feature-based, anatomically constrained, and finite element-based algorithms were 4.6 (2.0), 7.4 (2.7), 7.2 (2.6), 6.3 (2.3), and 7.5 (4.0). In the follow-up comparison of the anatomically constrained DIR, the strategy with liverfocused rigid registration initialization, CC as similarity metric and liver as a controlling ROI had the lowest mean TRE -6.0 (2.0). The maximum TRE for all techniques exceeded 10 mm. Selection of DIR strategy was found to be a statistically significant factor for registration accuracy. Tumor volume change had a significant effect on TRE for finite element...

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Section: Discussionmentioning

confidence: 99%

Accuracy of deformable image registration techniques for alignment of longitudinal cholangiocarcinoma CT images

et al. 2020

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Hyperspectral imaging in color vision research: tutorial

Foster

Amano

2019

J. Opt. Soc. Am. A

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Robust image registration for analysis of multisource eye fundus images

López-Jasso,

Felipe-Riverón,

Valdez-Rodríguez

2024

IFS

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This study underscores the crucial role of image preprocessing in enhancing the outcomes of multimodal image registration tasks using scale-invariant feature selection. The primary focus is on registering two types of retinal images, assessing a methodology’s performance on a set of retinal image pairs, including those with and without microaneurysms. Each pair comprises a color optical image and a gray-level fluorescein image, presenting distinct characteristics and captured under varying conditions. The SIFT methodology, encompassing five stages, with preprocessing as the initial and pivotal stage, is employed for image registration. Out of 35 test retina image pairs, 33 (94.28%) were successfully registered, with the inability to extract features hindering automatic registration in the remaining pairs. Among the registered pairs, 42.42% were retinal images without microaneurysms, and 57.57% had microaneurysms. Instead of simultaneous registration of all channels, independent registration of preprocessed images in each channel proved more effective. The study concludes with an analysis of the fifth registration’s resulting image to detect abnormalities or pathologies, highlighting the challenges encountered in registering blue channel images due to high intrinsic noise.

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Comparison of image registration methods for composing spectral retinal images

Cited by 3 publications

References 46 publications

Accuracy of deformable image registration techniques for alignment of longitudinal cholangiocarcinoma CT images

Accuracy of deformable image registration techniques for alignment of longitudinal cholangiocarcinoma CT images

Hyperspectral imaging in color vision research: tutorial

Robust image registration for analysis of multisource eye fundus images

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