Enhancement of nerve structure segmentation by a correntropy-based pre-image approach
| Autor: | Julián D. Echeverry-Correa, Andrés A. Álvarez-Meza, Álvaro Orozco-Gutiérrez, J. Gil-Gonzalez, Mauricio A. Álvarez-López |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Engineering
business.industry pre-images approximation lcsh:T Nerve structure segmentation Speckle noise Filter (signal processing) lcsh:Technology Visualization ultrasond images Sørensen–Dice coefficient Kernel (image processing) Discriminative model lcsh:TA1-2040 Code (cryptography) Correntropy Segmentation Computer vision Artificial intelligence business lcsh:Engineering (General). Civil engineering (General) |
| Zdroj: | TecnoLógicas, Vol 20, Iss 39 (2017) |
| ISSN: | 2256-5337 0123-7799 |
| Popis: | Peripheral Nerve Blocking (PNB) is a commonly used technique for performing regional anesthesia and managing pain. PNB comprises the administration of anesthetics in the proximity of a nerve. In this sense, the success of PNB procedures depends on an accurate location of the target nerve. Recently, ultrasound images (UI) have been widely used to locate nerve structures for PNB, since they enable a non-invasive visualization of the target nerve and the anatomical structures around it. However, UI are affected by speckle noise, which makes it difficult to accurately locate a given nerve. Thus, it is necessary to perform a filtering step to attenuate the speckle noise without eliminating relevant anatomical details that are required for high-level tasks, such as segmentation of nerve structures. In this paper, we propose an UI improvement strategy with the use of a pre-image-based filter. In particular, we map the input images by a nonlinear function (kernel). Specifically, we employ a correntropy-based mapping as kernel functional to code higher-order statistics of the input data under both nonlinear and non-Gaussian conditions. We validate our approach against an UI dataset focused on nerve segmentation for PNB. Likewise, our Correntropy-based Pre-Image Filtering (CPIF) is applied as a pre-processing stage to segment nerve structures in a UI. The segmentation performance is measured in terms of the Dice coefficient. According to the results, we observe that CPIF finds a suitable approximation for UI by highlighting discriminative nerve patterns. |
| Databáze: | OpenAIRE |
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