## 4 Results and Discussion
### Efficiency of Image Processing Tools  
For a qualitative comparison of the proposed thresholding-based segmentation technique with other standard methods in order to estimate the amount and kind of information to be retrieved and could be indicative for the pathology expert; a set of results from various thresholding methods is illustrated in Figure 5. The original test image is at 10 \(\upmu\)m slice depth. The following are brief descriptions of the methods used for comparison:  
* _Minimum thresholding_--assumes a bimodal histogram like the Otsu method and uses a running average method (of window size 3 \(\times\) 3) until 2 maxima are met.
* _Internodes thresholding_--also assumes a bimodal histogram and uses a running average to calculate two maxima. The threshold is selected as the average of these two maxima.
* _IsoData thresholding_--divides the image into object and background classes using an initial threshold. The next step is to calculate the average of the pixels below and above the threshold value. The mean of these values is calculated in order to update the threshold. The process is repeated until the threshold value is higher than the composite average [53].
* _Li thresholding_--uses Li's \"minimum cross entropy\" [54; 55], forming a reconstruction of the image distribution.
* _Shanblag thresholding_--assumes the image consisting of two fuzzy sets with each pixel assigned a membership function of belonging to one of the two classes (object and background). The segmentation is determined by the frequency of occurrence in each set [56].  
Figure 4: Illustration of tumor concentration at different slice depths (10 \(\upmu\)m, 40 \(\upmu\)m, 60 \(\upmu\)m, and 120 \(\upmu\)m), confirming the capacity of the utilized fluorescence imaging system to clearly depict and preserve the substantial tumor region information.  
The tissue border origin is indicated with a blue color, implying that the area under the marked curve reflects brain tissue. The tumorous cell locations are represented by high-intensity (white) segments, while the remaining (grey) area constitutes healthy tissue, discriminated from the cancerous area in terms of its decreased intensity. We notice that the minimum, intermodes, and Shanbhag thresholding approaches have missed a remarkable amount of information related to the tumor region (upper bright section of the image) because they have completely eliminated the background regions representing the healthy brain and intermediate tissue. In the IsoData result, the estimated cancerous section has been significantly increased with true positive pixels, yet the content of information is inferior to that of the entropy thresholding technique. In addition, the second class of pixels (corresponding to tissue background) has been eliminated as it is of limited importance. Finally, the method of entropy thresholding sustains a better signal to background ratio and improved contrast with respect to Li thresholding. Among the algorithmic schemes, entropy thresholding has proven efficient for the identification of brain tumors in several images tested and is the method selected for further consideration.