# Automatic Watershed Segmentation of Cancerous Lesions in Unsupervised Breast Histology Images
## 1 Introduction
In [12], a convolution neural network (CNN) model is used to identify and label nuclei cells into classes namely; nuclei, edges, and image background to form binary masks. The binary mask generated by the CNN model separates overlapping nuclei and non-overlapping objects, the Euclidean distance transform is used to segment edges and nuclei on the image. Watershed segmentation is used to segment nuclei on glands. This method uses markers to diminish and accentuate images ROI at specific locations as either foreground or background. A nuclei segmentation method proposed by [13] uses stain separation to obtain foreground pixels of an image. After the staining separation procedure, the watershed segmentation method isolates overlapping nuclei based on various morphology changes observed on the histology images. These morphology changes are key to highlighting the size and spread of BC in the nucleus. In [14], a fully convolution-deep-neural architecture is utilized to segment nuclei cells. This technique uses image color normalization and standardization to highlight and balance the brightness, of nuclei features and tissue structure regions respectively. The Link-Net architecture is used to encode and decode images for nuclei segmentation. Hematoxylin and Eosin (H&E)image staining procedure presented by [15], normalizes and highlights the nucleus and tissue regions respectively. Image contrast is adjusted to highlight and extract the nucleus from the image background and other uninterested regions. Watershed segmentation is done on the extracted nucleus region to remove, overlapping and unrelated objects not captured by the previous contrast adjustment step. A novel architecture by [16] excludes intersecting pixels of overlapped nuclei objects to form a new foreground image. Morphological operations are applied to the new image to ascertain edges, nuclei are labeled using markers, and the final result is segmented via watershed. Content-based image retrieval algorithm proposed by [17], pre-processes images by normalization techniques; histogram equalization, and object size kernel to identify nuclei objects. Thresholding and iterative morphology operations remove overlapping objects highlighting the nucleus centers and edges. The final image is a segmented region combining the nuclei, nuclei centers, and their edges/boundaries. While watershed segmentation is efficient computationally, it is devoid of over-segmentation when used on histology images. A technique proposed by [18] introduces improvements to the watershed segmentation method to deal with the over-segmentation of nuclei cells in histology images. In their pre-processing stage, Gaussian filters were used to remove noise and enhancement of nuclei cells. Morphological operations namely; opening and closing were used to highlight and accentuate both background and foreground pixels while maintaining their edges on the image. The image is further enhanced via OTSU thresholding and dilation morphology to outline the foreground and avoid overlapping nuclei objects on the image. Lastly, the foreground and background markers obtained from the previous step are segmented via the watershed segmentation method to obtain nuclei edges. Another proposed method to deal with over-segmentation is by [19], which initially separates H&E stains on the histology image via color de-convolution methods. Morphological operations namely; OTSU thresholding, opening, and closing to distinguish the foreground from the background and highlight the nuclei objects on the image. Distance transform is further used to identify touching nuclei objects and watershed segmentation to separate the touching nuclei objects while maintaining their edge boundaries. In [20], a nuclei segmentation method is used that pre-processes images via color unmixing to separate H&E stains on images. Morphology operations namely; opening to remove unconnected foreground, and background pixels, while closing to deal with irregular shapes of nuclei objects.