Object localization using non-Euclidean metrics
In this paper, we proposed to use non-Euclidean statistical metrics to localize multiple 3D anatomical structures by estimating the object’s position, orientation, and size in medical images. Precise orientation estimation is extremely important especially for model-based image segmentation algorithms as even a very small change in shape model orientation can lead to inaccurate localization and segmentation. We statistically evaluated accuracy of orientation estimation using various metrics: Euclidean, Mean Hermitian, Log-Euclidean, Root-Euclidean, Cholesky decomposition, and Procrustes Size-and-Shape. Experimental results showed that non-Euclidean metrics, particularly Mean Hermitian and Cholesky decomposition, provided more accurate estimates than Euclidean metrics. We presented the effectiveness of the proposed method using abdominal and hand computed tomography (CT) images and magnetic resonance (MR) images of the foot.
Funding
This study is supported by NIH R01-CA246704, R01-CA240639, R15- EB030356, R03-EB032943, and U01-DK127384-02S1.
History
School
- Science
Department
- Mathematical Sciences
Published in
2023 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) ProceedingsSource
2023 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)Publisher
IEEEVersion
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher statement
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Acceptance date
2023-10-01Publication date
2023-12-14Copyright date
2023ISBN
9798350319040; 9798350319057ISSN
2832-1375eISSN
2832-1383Publisher version
Language
- en