# Blob Detection and Deep Learning for Leukemic Blood Image Analysis
## 5 Experimental Results
### Results and Comparison with State-of-the-Art
Visual results are reported in Figure 13.  
It is crucial to notice that our approach was dependent only on three key parameters, which were the interval of the hue space, the blob dimension, and the value of \(t\), defined in Equation (3). We searched for blue objects; therefore, we tuned the range of hue accordingly, acting as a filter on the saturation channel image, and the same for every experiment. More precisely, in the [0-1] interval of the hue channel, where blue can be found around 2/3 of it, we selected the [0.65-0.9] range. By choosing a larger interval, the detection results could include artifacts (see Figure 14), to be removed in the next steps of the system, increasing the computational workload. The blob dimension, strictly related to WBC's size, was dependent on the acquisition condition of the images contained in the datasets, and one of them was sufficient to obtain the corresponding value. The last one was the \(t\) parameter. It was chosen to remove the unnecessary local maxima, to reduce or avoid \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **[PERSON] [7]** & **[PERSON] [9]** & **[PERSON] [6]** & **[PERSON] [22]** & **[PERSON] [23]** & **[PERSON] [24]** & **[PERSON] [25]** & **Our Method** \\ \hline N\({}^{\circ}\) images & 10 & Unknown & 33 & 33 & 14 & 33 & 33 & 33 \\ Accuracy & 81\% & - & - & 99.2\% & 94.2\% & - & 98.0\% & **99.7\%** \\ Precision & - & 94\% & - & 100\% & - & 89.0\% & 98.7\% & **100\%** \\ Recall & - & 88\% & 92\% & 99.2\% & - & 98.0\% & 99.3\% & **99.6\%** \\ F-measure & - & - & - & 99.6\% & - & 93.0\% & 98.9\% & **99.8\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: WBC counting performances compared with the state-of-the-art. [PERSON]’s work [25] was tested on the entire ALL-IDB. The column for this work indicates the results obtained on the first 33 images. any false positive. Figure 15 describes the detection performances by varying the value of \(t\), defined in Equation (3), on the IUMS-IDB and the SMC-IDB datasets. This showed that the detection results were close (or equal) to 100% if the value of \(t\) was chosen in the range [0.4-0.6], avoiding or reducing the detection of false positives. In general, the detection performances were high even in a wider range of \(t\), but were the best in the interval mentioned above. In conclusion, we can state that our method could be generalized to datasets with different illumination conditions and resolutions, taking into account the characteristics of the parameters, easily obtainable from a sample image of each dataset. Figure 11: Pixel-wise segmentation accuracy for every tested image. The red line indicates an average accuracy of 97.02% on the first 33 images. Figure 12: Example of images with a pixel-wise segmentation accuracy lower than the average. The reason is related to the presence of a light cytoplasm, which makes segmentation harder than the nucleus region. From left to right: Image Numbers 9 and 30. Figure 10: Pixel-wise accuracy of cell segmentation with the proposed method. Objects accuracy is subdivided into intervals. The Pareto chart shows that our method offers high pixel-wise accuracy for the majority of the objects and that more than 90% of the total cells are segmented with a pixel-wise accuracy of more than 95%.