Name
Abstract | ||
|---|---|---|
In the field of medicine, ultrasound images have become a useful tool for visualizing nerve structures in the process of anesthesiology. Although, these images are commonly used in medical procedures such as peripheral nerve blocks. Their poor intelligibility makes it difficult for the anesthesiologists to perform this process accurately. Therefore, an automated segmentation methodology of the peripheral nerves can assist the experts in improving accuracy. This paper proposes a peripheral nerve segmentation method in medical ultrasound images, based on Speckle removal and Bayesian shape models. The method allows segmenting efficiently a given nerve by performing a Bayesian shape fitting. The experimental results show that performing a speckle removal before fitting the model, improves the accuracy due to the enhancement of the image to segment. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-19390-8_44 | PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2015) |
Keywords | Field | DocType |
Peripheral nerve segmentaion, Speckle removal, Bayesian shape models | Computer vision,Peripheral,Speckle pattern,Pattern recognition,Computer science,Segmentation,Artificial intelligence,Shape fitting,Bayesian probability,Intelligibility (communication) | Conference |
Volume | ISSN | Citations |
9117 | 0302-9743 | 1 |
PageRank | References | Authors |
0.34 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hernán F. García | 1 | 6 | 3.62 |
| Juan J. Giraldo | 2 | 1 | 0.34 |
| Mauricio A. Álvarez | 3 | 165 | 23.80 |
| Álvaro Á. Orozco | 4 | 16 | 12.88 |
| Diego Salazar | 5 | 1 | 0.34 |