## 4 Results and Discussion
### Qualitative and Quantitative Comparisons with Other Segmentation Techniques  
To further validate the efficiency of the proposed unsupervised tumor-margin identification scheme, we compared our methodology in both qualitative and quantitative means with two other state-of-the-art segmentation approaches; the first method attempts to extract the tumor segments based on texture estimation and analysis utilizing the range filter [59], while the second reference method is based on the supervised k-means clustering scheme. The parameter k (number of clusters) of the algorithm was selected equal to 3, under the assumption that the fluorescence image is divided into three main areas, including the tumor region, healthy tissue region, and background. It is worth mentioning that our approach does not require specification of the number of regions of interest, which is automatically estimated via the mean shift clustering procedure. Figure 11 illustrates qualitative results by comparing the extracted images from the two reference schemes and the proposed approach on the same fluorescent image sample. Our method outperforms the other two in preserving the key information of the tumor region, while filtering out possible outliers originating from the ambiguity of intensity on the borders of healthy and tumorous tissue regions. In particular, texture analysis (Figure 11c) results in over-segmentation of cancerous tissue, whereas k-means clustering (Figure 11b) produces outliers at the margins of healthy tissue, which causes over-segmentation of the final tumor mask. The quantitative results of the comparison between the proposed methodology and the reference methodologies are summarized in Table 3. The estimated tumor margins, as a binary mask derived from each technique, are compared with respect to the ground truth borders overlaid by the medical expert who evaluated the fluorescence tissue samples. Classification metrics of success, including accuracy, precision, recall, specificity, dice coefficient (DC), and intersection over union (IoU) [60], are calculated (on the image dataset derived from the one tissue sample at difference slice depths) in order to evaluate the efficiency of our approach and justify its potential over other techniques in effectively classifying tumor areas and identifying their borders. \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 \(\mu\)m tumor** & 0.8605 & 0.9110 & 0.0782 & 0.7396 & 0.0087 & 2.9945 & 7.5119 & 114.7772 \\ \hline
**10 \(\mu\)m normal** & 0.2345 & 0.4860 & 0.3664 & 0.8827 & 0.0532 & 2.2848 & 5.1972 & 18.7808 \\ \hline
**30 \(\mu\)m tumor** & 1.0422 & 0.8850 & 0.0546 & 0.7037 & 0.0083 & 2.5526 & 7.7284 & 120.6932 \\ \hline
**30 \(\mu\)m normal** & 0.2786 & 0.4538 & 0.3052 & 0.8611 & 0.0396 & 1.9403 & 5.4884 & 25.2802 \\ \hline
**40 \(\mu\)m tumor** & 1.0965 & 0.8406 & 0.0755 & 0.7065 & 0.0104 & 3.4550 & 7.4370 & 96.1131 \\ \hline
**40 \(\mu\)m normal** & 0.3542 & 0.3240 & 0.2705 & 0.8235 & 0.0426 & 2.3261 & 5.4435 & 23.4819 \\ \hline
**60 \(\mu\)m tumor** & 1.2161 & 0.8373 & 0.0662 & 0.6838 & 0.0092 & 2.9578 & 7.6068 & 108.2671 \\ \hline
**60 \(\mu\)m normal** & 0.4129 & 0.2522 & 0.2800 & 0.8042 & 0.0326 & 2.0038 & 5.7292 & 30.7130 \\ \hline
**100 \(\mu\) tumor** & 1.0020 & 0.8704 & 0.0774 & 0.7195 & 0.0098 & 3.4370 & 7.4428 & 101.7404 \\ \hline
**100 \(\mu\) normal** & 0.2929 & 0.4249 & 0.3084 & 0.8556 & 0.0442 & 2.3936 & 5.2471 & 22.6216 \\ \hline
**120 \(\mu\) tumor** & 0.7467 & 0.8694 & 0.1385 & 0.7653 & 0.0127 & 5.0570 & 7.9839 & 78.7948 \\ \hline
**120 \(\mu\) normal** & 0.3060 & 0.4122 & 0.2848 & 0.8473 & 0.0412 & 2.1896 & 5.4521 & 24.2514 \\ \hline
**150 \(\mu\) tumor** & 1.1291 & 0.8496 & 0.0616 & 0.6841 & 0.0090 & 2.8176 & 7.6219 & 111.5111 \\ \hline
**150 \(\mu\) normal** & 0.3916 & 0.2236 & 0.3157 & 0.8133 & 0.0321 & 1.8078 & 5.6469 & 31.1538 \\ \hline \hline \end{tabular}
\end{table}