Foreground segmentation of human insulin crystal images for in-situ microscopy

In this paper, an algorithm is introduced for segmenting the foreground regions present in a human insulin crystal intensity image captured by an in-situ microscope inside of a bioreactor. The segmentation is carried out by classifying all image pixels into pixels belonging to the foreground regions and pixels belonging to the background region. For classification, the local intensity variance at each pixel position is compared to a threshold. Those pixels whose local intensity variance is bigger than the threshold are classified as belonging to the foreground regions. The threshold is estimated as a linear combination of two statistical characteristics of the local intensity variance values at the pixels in the background region. Those statistical characteristics are estimated from the histogram of the local intensity variance values of all image pixels by maximizing a likelihood function using an Expectation and Maximization approach. Misclassifications are corrected by particle filtering. Experimental results on real data revealed a processing time of 11.82 seconds/image, an excellent reliability and a segmentation error of approximately 14 pixels.