# Automatic Watershed Segmentation of Cancerous Lesions in Unsupervised Breast Histology Images
## 1 Introduction  
Early diagnosis of breast cancer (BC) relies heavily on how cancerous lesions spread on nuclei and tissue glands and thus offers a need for a prognosis. Traditionally, the Bloom-Richardson grading system is used by clinicians to determine the grade of how tumor cells are morphing into normal nuclei the degree of morphing, and how the tumors are increasing in number [1]. The grading system is done by examining cells through a microscope, which is limited in observing other important features. With this deficiency, automatic-aided imaging is preferred and assists in analyzing nuclei cells in BC histology images. Research authors in [2] propose an automatic segmentation method for BC via pre-processing images through median filtering, rotation, and identification of pectorals via highlighting the region of interest (ROI). The watershed algorithm is then used to divide the image into foreground and background via sobel filters, thresholding, and distance maps to remove uninterested regions of the image. Overlapping of objects is observed with this method and authors in [3] reiterate the need for early breast cancer detection through their proposed method. The proposed method uses image processing and watershed algorithmsto segment breast mammogram images through density estimation, contour extraction, and region segmentation to assist BC detection. In [4], image pre-processing is handled via median filtering to remove noise, morphology operations using gray scaling, active contours to highlight and accentuate foreground and background regions, and watershed segmentation to merge different image regions and their boundaries. Image pre-processing based on color, texture, and gradient intensity through canny, Sobel, and blob edge detectors is presented by the method in [5]. Watershed segmentation separates overlapping/touching objects by dilation and erosion morphology operations, and cancerous tumors extracted from the remaining object features. A CAD system proposed by [6] pre-processes ultra-sound breast images to improve image brightness, and image segmentation through the marker-controlled watershed to visualize topographically interested lesion regions and their edge boundaries on the image. Segmentation of cells can also be used to locate tumor regions of interest (ROI) within the image. However, nuclei segmentation has several challenges namely; improper image pre-processing, the complexity of tissue structures, overlapping cells, uneven color distribution caused by the staining procedure, and scanning equipment irregularities [7]. Conventionally, thresholding and morphology operations have been the most widely used techniques for segmenting medical images. For instance, ref. [8] proposes an adaptive deconvolution algorithm to color normalize images in a dataset, use morphology operations to pre-process the images, threshold them, and further post-process these images to detect edges. Consequently, ref. [9] proposes an automated segmentation technique that uses gray scaling, median filtering, and bottom-top hat filters as pre-processing steps to enhance image contrast. Thresholding is used to identify regions of interest, while post-processing morphology techniques namely; dilation, area opening, and filling in holes are used to improve final segmentation results. Further, a novel method proposed by [10] uses wavelet transform decomposition to highlight and enhance regions of interest and edge boundaries on mammogram images. Thresholding and binary morphological operations namely; dilation and erosion are used to identify the breast ROI, masking the ROI to isolate it from unwanted pectoral muscle regions. In [11], image pre-processing is handled through normalization, segmentation through color deconvolution for nuclei enhancement, and data augmentation to increase dataset size. A binary threshold was used to aid in detecting nuclei edges in the images. Watershed segmentation is a common remedy for overlapping objects on medical images resulting from data preparation and pre-processing.