A robust segmentation method for counting bovine milk somatic cells in microscope slide images

• A two-step methodology for background removal of bovine milk images is proposed.
• A well-tuned k-means algorithm removes all sort of debris from background.
• A new thresholding removes the debris-free and statistically varying background.
• The background-removed cells under Watershed transform are robust-segmented.
• High accuracy somatic cell counting algorithm for bovine milk quality control.

Mastitis is an infectious disease associated with the increased number of somatic cells in cow’s milk, and it is one of the most relevant cause of economic losses in dairy farming industries. In this paper, we propose a method capable of determine, with 99.7% accuracy, the number of these cells in microscope slide images. This level of accuracy is achieved by changing the image original RGB format to Lab color space and applying k-means clustering algorithm to remove debris and other background features. A new gray level thresholding is proposed, and the remaining bound cells are separated in the final segmentation step applying Watershed transform. Many microscope slide images with debris, contrast, and hue variation were used to validate the experimental results. Comparison between the proposed method and manual counting indicates that this new approach is a robust and promising solution to be incorporated in a future automated somatic cell counting system using optical microscopy.