Detection of egg stains based on local texture feature clustering

The quality of egg is mainly influenced by the dirt adhering to its shell. Even with good farm-management practices and careful handling, a small percentage of dirty eggs will be produced. The purpose of this research was to detect the egg stains by using image processing technique. Compared to the color values, the local texture was found to be much more adept at accurately segmenting of the complex and miscellaneous dirt stains on the egg shell. Firstly, the global threshold of the image was obtained by two-peak method. The irrelevant background was removed by using the global threshold and the interested region was acquired. The local texture information extracted from the interested region was taken as the input of fuzzy C-means clustering for segmentation of the dirt stains. According to the principle of projection, the area of dirt stains on the curved egg surface was accurately calculated. The validation experimental results showed that the proposed method for classifying eggs in terms of stain has the specificity of 91.4% for white eggs and 89.5% for brown eggs.