# Automatic Watershed Segmentation of Cancerous Lesions in Unsupervised Breast Histology Images
## 3 Results and Discussion  
The experimental results of this study are based on an analysis of the entire automatic watershed segmentation process. Experiments were carried out, on 11,151 unsupervised BC (normalized H) histology images split into 8079 training and 2694 testing set images. Processing of the unsupervised BC dataset to aid data analysis has been discussed in previous sections of this work. Figure 10: Unsupervised BC (normalized H) histology images result from this proposed watershed segmentation method. Figure 9: Connected component images. Figure 1 shows an overview of the processing stages of the proposed segmentation method namely; pre-processing, image enhancement, Segmentation, and feature extraction. Additionally, the performance evaluation of the proposed approach is based on its automatic ability to segment cancerous lesions on (normalized H) breast histology images shown by Figure 10.  
Figure 11 illustrates visual examples of images before and after the application of our proposed segmentation method. These image results demonstrate the effectiveness of the proposed method on other human glands histology images gathered from the publicly available Warwick QU Dataset in the Kaggle dataset repository. Therefore, this proposed method is efficient in segmenting and maintaining nuclei objects and their edges on histology images, with this clearly illustrated from the various image results of other human glands namely; kidney and colon H&E images among others provided in the publicly available Warwick dataset. Breast cancerous lesions are detected via their spread on tissues signifying nuclei objects irregularity on histology images. The H&E stain separation and normalization technique aids the dividing of image pixels into hematoxylin (blue color) denoting nuclei features, whereas eosin (pink color) denotes tissue structures. Therefore, any other breast lesion on either lobules or ducts will be detected by separating nuclei objects from non-nuclei objects in histology images, thus the applicability of the proposed approach. Table 1 shows the performance improvement caused by introducing the proposed watershed segmentation method. Our proposed technique achieved significant results in identifying and highlighting BC lesions throughout the unsupervised (normalized H) histology image dataset. These results are attributed to various reasons namely; dataset augmentation that increases the dataset size, and stain normalization reducing image color inconsistencies. Table 2 shows the comparison between, our proposed approach and other watershed-related techniques that focus on segmenting nuclei objects on human histology images. Figures 12 and 13 present training loss and accuracy graph curves and validation loss and accuracy graph curves of the proposed method. \begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & **Augmented Images** & **After Watershed Images** \\ \hline
**Accuracy** & **95\%** & **98\%** \\ \hline \end{tabular}
\end{table} 
Table 1: Performance evaluation results before and after applying watershed segmentation method. \begin{table}
\begin{tabular}{c c} \hline
**Reference Authors** & **Watershed-Related Methods** & **Accuracy** \\ \hline [PERSON] et al. [7] & Marker-controlled watershed & 81.5\% \\ [PERSON] et al. [13] & Marker-based watershed & 92\% \\ [PERSON] et al. [8] & Adaptive deconvolution+multi-level thresholding & 94.6\% \\ [PERSON] et al. [9] & Thresholding+ Post processing morphology operations & 93.5\% \\ [PERSON] et al. [11] & DenseResidual-Unets & 90.03\% \\ [PERSON] et al. [12] & CNN+Watershed transform & 83.4\% \\ [PERSON] et al. [14] & LinkNet Deep neural network & 97.2\% \\ [PERSON] et al. [15] & Morphology operations+ Adaptive watershed transform & 75\% \\ [PERSON] et al. [16] & Deep Convolution neural networks+ Marker-controlled watershed & 87.8\% \\ [PERSON] et al. [17] & Content based Image retrieval algorithm & 94.4\% \\
**Proposed Method** & **Masked-watershed +RNN** & **98\%** \\ \hline \end{tabular}
\end{table}