Patch similarity in ultrasound images with hypothesis testing and stochastic distances
| Autor: | Nelson D. A. Mascarenhas, Cid A. N. Santos |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Noise reduction Health Informatics Signal-To-Noise Ratio Measure (mathematics) 030218 nuclear medicine & medical imaging 03 medical and health sciences Speckle pattern symbols.namesake 0302 clinical medicine Similarity (network science) Image Processing Computer-Assisted Radiology Nuclear Medicine and imaging Segmentation Ultrasonography Statistical hypothesis testing Stochastic Processes Radiological and Ultrasound Technology business.industry Pattern recognition Computer Graphics and Computer-Aided Design Euclidean distance Computer Science::Graphics Gaussian noise Computer Science::Computer Vision and Pattern Recognition symbols Computer Vision and Pattern Recognition Artificial intelligence business Algorithms 030217 neurology & neurosurgery |
| Zdroj: | Computerized Medical Imaging and Graphics. 74:37-48 |
| ISSN: | 0895-6111 |
| DOI: | 10.1016/j.compmedimag.2019.03.001 |
| Popis: | Patch-based techniques have been largely applied to process ultrasound (US) images, with applications in various fields as denoising, segmentation, and registration. An important aspect of the performance of these techniques is how to measure the similarity between patches. While it is usual to base the similarity on the Euclidean distance when processing images corrupted by additive Gaussian noise, finding measures suitable for the multiplicative nature of the speckle in US images is still an open research. In this work, we propose new stochastic distances based on the statistical characteristics of speckle in US. Additionally, we derive statistical measures to compose hypothesis tests that allow a quantitative decision on the patch similarity of US images. Good results with experiments in denoising, segmentation and selecting similar patches confirm the potential of the proposed measures. |
| Databáze: | OpenAIRE |
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