# Neural Network Ensemble to Detect Dicentric Chromosomes in Metaphase Images
## 3 Experimental Results
### Preprocessing
#### 3.2.1 Binarization and Thresholding Techniques  
For the selection of the threshold value, two different techniques have been employed, one of our own devising and another applied in a paper also focused on the detection of dicentric chromosomes through a two-stage convolutional network that uses a single type of image preprocessing [14].  
Our process consisted of obtaining image histograms (plots showing the number of pixels present for each intensity value) and plotting a representative polynomial (hereafter a Spline curve) of the discrete set of points through interpolation. On this curve, the points representing local maxima were identified. To determine the threshold value for the binarization process, the chosen value was selected from the valley between the peaks. (see Figure 3). To fix the values for the binarization parameters with the methods Spline and Otsu, samples from the first split (only ten images) of the dataset were selected.  
When we observed that one of the selected images exhibited three peaks (local maxima), unlike most images with two peaks, we carefully analyzed the whole dataset for our own research interest and found that only two cases from the whole dataset (50 images) displayed three peaks. In 48 cases, two thresholds appear: the first from the black peak (which corresponds to the chromosomes), and the second from the white peak (which corresponds to the background). Binarization was performed using the threshold derived from the black peak. Only two cases in the whole dataset (images 2 Gy-068 and 2 Gy-360) showed three local maxima, and therefore three thresholds observed (see Figure 4). In both cases, two of these thresholds corresponded to black peaks, and only the binarization using the threshold generated from the largest black peak (i.e., the peak with the largest  
Figure 2: Example of two images from the dataset, where normal chromosomes and chromosomes with two centromeres (dicentric) are presented.  
number of pixels) produced acceptable results (recognizable chromosomes), regardless of whether the lower black peak was to the left or right of the upper black peak. Taking this into account, the threshold selection was the one generated by the maximum of the peaks, excluding the last peak (which is always higher in the number of pixels since it corresponds to the background).  
Once binarization was applied, it was observed that, although in most chromosomes, the thresholding allowed perfect recognition and even to distinguish between dicentric and non-dicentric chromosomes, the threshold was significantly displaced to the left in some images, rendering these images unsuitable for training the model (even though the threshold is obtained when a valley of the histogram is reached).  
On the other hand, the results obtained from thresholding with [PERSON]'s method (used in the previously mentioned study [14]) were studied, while using [PERSON]'s algorithm to set the threshold solves the problem that arose when using the Spline technique alone. It should be noted that the separation between sister chromatids is small--zero in many of the chromosomes--and that this could be an obstacle to the model in discerning between dicentric and non-dicentric chromosomes. A comparison between the methods is presented in the first row of Figure 5.  
Figure 4: Histogram of images 2 Gy-068 and 2 Gy-360 with Spline curve and thresholds obtained from local maxima (black peak on the **left** and white peak on the **right**). Note that three peaks are presented.  
Figure 3: Histogram of image 2 Gy-023 with Spline curve and thresholds obtained from local maxima (black peak on the **left** and white peak on the **right**).