## 4 Results and Discussion
### Evaluation of Detection Results
Finally, it should be emphasized that the binary mask constructed via segmentation fusion and morphology refinement does not underestimate the cancerous region but rather enriches it with additional depth penetration, providing adequate information regarding its geometrical formulation and setting the prerequisites for complete tumor resection. In order to further support the information and outcomes of the previous figures and analysis, statistical texture results were calculated, pertaining to the raw brain tumor images obtained by fluorescence microscopy in lower tumor slice depths (10 \(\upmu\)m, 40 \(\upmu\)m, and 40 \(\upmu\)m) and the corresponding processed images via the proposed three-step segmentation approaches. Table 1 summarizes statistical texture parameters, namely contrast, correlation, energy, homogeneity, skewness, kurtosis, entropy, and standard deviation, calculated for the images to quantify their texture properties. The texture is the pattern or spatial arrangement of different pixels possessing different intensity levels, describing the structure of the object in the image. The texture of an image is generally not uniform due to the variations in intensity levels and the spatial relationship of pixels, which are characterized by the smoothness/roughness or uniformity of the object in the image. Texture can also vary due to the differences in the reflectance of the \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Image** & **Contrast** & **Correlation** & **Energy** & **Homogeneity Skewness** & **Kurtosis** & **Entropy** & **Std. Deviation** \\ \hline
**10 \(\upmu\)m Original** & 0.621 & 0.8596 & 0.3572 & 0.8616 & 0.0286 & 20.9983 & 5.0294 & 34.9634 \\ \hline
**10 \(\upmu\)m—Otsu threshold** & 0.994 & 0.7865 & 0.8167 & 0.9552 & 0.0229 & 27.0335 & 2.4751 & 43.7297 \\ \hline
**10 \(\upmu\)m—Entropy threshold** & 1.8217 & 0.6992 & 0.8274 & 0.9623 & 0.0177 & 29.2198 & 1.5077 & 56.5694 \\ \hline
**10 \(\upmu\)m—Mean shift Segmented** & 1.0430 & 0.7824 & 0.8739 & 0.9746 & 0.0192 & 27.9363 & 1.6581 & 52.2174 \\ \hline
**30 \(\upmu\)m Original** & 0.6152 & 0.8381 & 0.2244 & 0.8008 & 0.0203 & 11.620 & 5.2582 & 49.252 \\ \hline
**30 \(\upmu\)m—Otsu thresholded** & 0.8270 & 0.7257 & 0.7125 & 0.9252 & 0.0194 & 17.0752 & 2.9147 & 54.4028 \\ \hline
**30 \(\upmu\)m—Entropy thresholded** & 1.7456 & 0.6650 & 0.7933 & 0.9615 & 0.0151 & 19.236 & 1.5958 & 66.1713 \\ \hline
**30 \(\upmu\)m—Mean shift Segmented** & 1.1012 & 0.7822 & 0.8275 & 0.9650 & 0.0179 & 18.3914 & 1.9194 & 62.8654 \\ \hline
**40 \(\upmu\)m Original** & 0.3457 & 0.8073 & 0.4200 & 0.8712 & 0.0277 & 18.994 & 4.7985 & 36.1215 \\ \hline
**40 \(\upmu\)m—Otsu thresholded** & 0.9510 & 0.7452 & 0.7412 & 0.9142 & 0.0196 & 20.1411 & 3.2974 & 55.1103 \\ \hline
**40 \(\upmu\)m—Entropy thresholded** & 1.575 & 0.6352 & 0.9023 & 0.9803 & 0.0123 & 28.666 & 1.9651 & 58.412 \\ \hline
**40 \(\upmu\)m—Mean shift Segmented** & 1.1139 & 0.8022 & 0.9159 & 0.9845 & 0.0159 & 22.7491 & 1.9418 & 51.9204 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical quantification of image texture for different segmentation results. surface being imaged. The texture of an image region can vary if it has different local statistics with respect to its intensity levels. Different texture metrics indicate different image properties/characteristics quantifying the textural information of the surface via the distribution of the pixel intensities and the involving statistics. A detailed description of the texture analysis and the theoretical and mathematical background can be found in [57, 58]. Regarding the calculation of the standard deviation metric, this is performed on the intensity values of the original images within the regions (logical AND operation, i.e., only pixels of the original image that are \"active\" within the segmentation result) extracted by the corresponding segmentation techniques examined.