| Autor: |
Savio, Sami, Harrison, Lara, Ryymin, Pertti, Dastidar, Prasun, Soimakallio, Seppo, Eskola, Hannu |
| Zdroj: |
Proceedings of SPIE; March 2011, Vol. 7962 Issue: 1 p79622C-79622C-10, 7882589p |
| Abstrakt: |
In this study, we have studied the effect of background noise on the texture analysis of muscle, bone marrow and fat tissues in 1.5 T magnetic resonance (MR) images using different statistical methods. Variable levels of noise were first added on 3-mm thick T2 weighted image slices of voluntary subjects to simulate several signal-to-noise ratio (SNR) levels. For each original and simulated image, the values for 264 texture parameters were calculated using MaZda, a texture analysis toolkit. We also determined Fisher coefficients based on the texture parameter values in order to enable high discrimination between different tissues. Linear discriminant analysis (LDA) and two different nearest neighbour (NN) methods were then applied for the texture parameters with the highest Fisher coefficient values. Several training and test sets were used to approximate the variation in the classification results. All the above-mentioned methods had the same classification accuracy, which in turn depended on the image SNR. We conclude that these tissues can be detected by texture analysis methods with a sufficient accuracy (90%) especially if SNR is at least 30-40 dB, even though the separation of different muscles remains a very challenging task. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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