# Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol
## 4 Discussion  
An interesting aspect when analysing the results was the features returning the best characteristics. In all experiments, one feature was derived from the GLCM matrix, while the other was concentrating on the grey level distribution (like Hist, Arm features) or gradient orientation (Gab, Haar). It is interesting to note that the GLCM was more descriptive when the number of bits decreased (in most cases, 4 or 5 bits were used), resulting in less sparse inner matrices to calculate the features. In the case of grey-level intensity levels and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Accuracy** & **Precision** & **Recall** & **F1-Score** & **Feature 1** & **Feature 2** \\ \hline
0.7500 & 0.8788 & 0.6042 & 0.7161 & YTM4 GlcmZ3 Contrast & YDBRHistMean \\
0.7500 & 0.8485 & 0.6087 & 0.7089 & YMG6 lcmN4 SumEntrp & YDSArmTeta4 \\
0.7500 & 0.8485 & 0.6087 & 0.7089 & YMG5 lcmZ5 Entropy & YDSArmTeta4 \\
0.7391 & 0.8788 & 0.5918 & 0.7073 & YMG6 lcmZ5 SumEntrp & YDSArmTeta4 \\
0.7500 & 0.8182 & 0.6136 & 0.7013 & YMG7 GlcmZ4 SumVarnc & YDSArmTeta4 \\
0.7500 & 0.8182 & 0.6136 & 0.7013 & YMG6 lcmZ4 SumVarnc & YDSArmTeta4 \\
0.7500 & 0.8182 & 0.6136 & 0.7013 & YMG5 lcmH4 SumEntrp & YDSArmTeta4 \\
0.7500 & 0.8182 & 0.6136 & 0.7013 & YMG5 lcm24 SumVarnc & YDSArmTeta4 \\
0.7391 & 0.8485 & 0.5957 & 0.7000 & YMG6 lcmH2 SumEntrp & YDSArmTeta4 \\
0.7283 & 0.8788 & 0.5800 & 0.6988 & YMG5 lcmN2 SumVarnc & YDSArmTeta4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The best 10 pair of features when both (central and peripheral) regions are used to calculate features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Accuracy** & **Precision** & **Recall** & **F1-Score** & **Feature 1** & **Feature 2** \\ \hline
0.8152 & 0.8485 & 0.7000 & 0.7671 & YN7 GlcmZ3 DifEntrp & YSSArmTeta2 \\
0.7609 & 0.9394 & 0.6078 & 0.7381 & YS7 ArmTeta2 & YDSGradMean \\
0.7500 & 0.9394 & 0.5962 & 0.7294 & YS8 ArmTeta2 & YDSGradMean \\
0.7500 & 0.9091 & 0.6000 & 0.7229 & YS6 ArmTeta2 & YDSHistSkewness \\
0.7391 & 0.9394 & 0.5849 & 0.7209 & YS6 ArmTeta2 & YDSGradMean \\
0.7609 & 0.8485 & 0.6222 & 0.7180 & YN7 GlcmN4 DifEntrp & YSSArmTeta2 \\
0.7500 & 0.8788 & 0.6042 & 0.7161 & YS7 GlcmN4 DifEntrp & YSSArmTeta2 \\
0.7500 & 0.8788 & 0.6042 & 0.7161 & YS6 GlcmN5 DifEntrp & YSSArmTeta2 \\
0.7500 & 0.8788 & 0.6042 & 0.7143 & YM4 GlcmH1 SumVarnc & YDSGab12V6 Mag \\
0.7391 & 0.8788 & 0.5918 & 0.7073 & YSSGlcm23 DifEntrp & YSSArmTeta2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The best 10 pair of features when central zone is used to calculate the features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Accuracy** & **Precision** & **Recall** & **F1-Score** & **Feature 1** & **Feature 2** \\ \hline
0.7363 & 0.8750 & 0.5833 & 0.7000 & YS6 GlcmV3 SumEntrp & YDSHistMean \\
0.7253 & 0.9063 & 0.5686 & 0.6988 & YS6 GlcmV3 InvDMom & YDBRHistMean \\
0.7363 & 0.8125 & 0.5909 & 0.6842 & YN5 ArmTeta2 & YDBRHistMean \\
0.7253 & 0.8438 & 0.5745 & 0.6835 & YN4 GlcmN3 InvDMom & YDBRHistMean \\
0.7363 & 0.7813 & 0.5952 & 0.6757 & YN5 GlcmV3 DiffEntrp & YDBRHistMean \\
0.7143 & 0.8438 & 0.5625 & 0.6750 & YM4 GlcmN3 SumOfSq & YSSDwtHaarSIHH \\
0.7033 & 0.8750 & 0.5490 & 0.6747 & YN5 GlcmH4 Entropy & YDSHistKurtosis \\
0.7033 & 0.8750 & 0.5490 & 0.6747 & YN7 HogQ8b3 & YM8 HistPerc01 \\
0.7033 & 0.8438 & 0.5510 & 0.6667 & YM4 GlcmH3 DiffEntrp & YDBRHistMean \\
0.6813 & 0.9063 & 0.5273 & 0.6667 & YN6 GlcmH2 SumEntrp & YDSHistMean \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The best 10 pairs when peripheral zone is used to calculate the features. gradients, mostly 8-bit data were applied. The exception was only when the whole region analyses were considered with YD5 ArmTeta4 feature. It is difficult to draw any conclusions regarding the influence of data normalization on the results, as we can see features derived with all possibilities. Figure 3 presents compactly 500 best scores achieved in each case.