Name
Title | ||
|---|---|---|
Fully automatic prostate segmentation in MR images using a new hybrid active contour-based approach. |
Abstract | ||
|---|---|---|
Precise prostate segmentation in magnetic resonance (MR) images is mostly utilized for prostate volume estimation, which can help in the determination of prostate-specific antigen density. In this paper, a fully automatic method that contains three successful steps to segment the prostate area in MR images is presented. This method includes a preprocessing stage, an automatic initial point generation step and an active contour-based algorithm with an external force known as vector field convolution (VFC). First, both noise and roughness are approximately removed using Sticks filter and morphology smoothing method. Then, an initial point is automatically generated using multilayer perceptron neural network to initiate the segmentation algorithm. Finally, VFC is employed to extract the prostate region. This system was tested on image data sets to detect the prostate boundaries. Results show that the proposed method can reach a DSC value of \({86~\pm ~6\%}\), is faster than existing methods and also more robust as compared to other methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11760-018-1320-y | Signal, Image and Video Processing |
Keywords | Field | DocType |
Neural network, Active contour, Prostate segmentation, Sticks filter, Morphology smoothing | Active contour model,Computer vision,Data set,Pattern recognition,Segmentation,Prostate segmentation,Preprocessor,Smoothing,Artificial intelligence,Volume estimation,Artificial neural network,Mathematics | Journal |
Volume | Issue | ISSN |
12 | 8 | 1863-1703 |
Citations | PageRank | References |
1 | 0.35 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahad Salimi | 1 | 1 | 0.68 |
| Mohammad Ali Pourmina | 2 | 107 | 9.75 |
| Mohammad Shahram Moin | 3 | 10 | 2.12 |