Denoising of Ultrasound Medical Images Using the DM6437 High-Performance Digital Media Processor
Medical ultrasound images are inherently contaminated by a multiplicative noise called speckle. The noise reduces the resolution and contrast, decreasing the capability of the visual evaluation of the image, and sometimes small speckles can mask ills in early stages. Therefore, denoising plays an im...
| Autores: | , , |
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| Formato: | capítulo de livro |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | México |
| Recursos: | Universidad Autónoma de Ciudad Juárez |
| Repositorio: | Repositorio Institucional de la Universidad Autónoma de Ciudad Juárez |
| OAI Identifier: | oai:uacj.mx:oai:cathi.uacj.mx:20.500.11961ir-4624 |
| Acesso em linha: | https://doi.org/10.1007/978-3-319-77770-2_1 |
| Access Level: | acceso abierto |
| Palavra-chave: | Denoising Ultrasound medical images Digital signal processor Filtering info:eu-repo/classification/cti/7 |
| Resumo: | Medical ultrasound images are inherently contaminated by a multiplicative noise called speckle. The noise reduces the resolution and contrast, decreasing the capability of the visual evaluation of the image, and sometimes small speckles can mask ills in early stages. Therefore, denoising plays an important role in the diagnostic. Many investigations reported in the literature claim their performance. However, this is limited because the unclear indicators or sometimes the algorithms proposed are not suitable for implementations in hardware. In this chapter, the implementation of five methods, specifically designed to reduce multiplicative noise, in a digital signal processor is presented. The chapter includes performance evaluation of each method implemented in a fixed point, DM6437 digital signal processor (digital media processor) of Texas Instruments™. Results show that the performance of the Frost and Lee filters, with a local window of 5 × 5 pixels, is better to reduce high-variance speckle noise than the rest of the filters. For noise variance less than 0.1, the SRAD with 15 iterations has a higher performance. However, the Frost and SRAD filters take more time to yield a result. |
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