Automating microscope colour image analysis using the Expectation Maximisation algorithm

Dyed barley cells in microscope colour images of biological experiments are analysed for the occurrence of haustoria of the powdery mildew fungus by a fully automated screening system. The region of interest in the images is found by applying Canny’s edge detector to the hue channel of the HSV colour space. Potential haustoria regions are extracted in RGB colour space by an adaptive Gaussian mixture classifier based on the Expectation Maximisation (EM) algorithm. Since the classes cell and haustorium are at very close quarters, their correct separation is a crucial part and needs a constraining mechanism which ties the EM algorithm to its initialisation data to prevent a too large deviation from it

Haustoria segmentation in microscope colour images of barley cells

Dyed barley cells in microscope colour images of biological experiments are analysed for the occurrence of haustoria of the powdery mildew fungus by a fully automated screening system. The region of interest in the images is found by applying Canny's edge detector to the hue channel of the HSV colour space. For the segmentation of potential haustoria within the dyed cells, two different methods are considered: A clustering in RGB colour space using the Expectation Maximisation (EM) algorithm, and morphological contrast enhancement of the colour image with subsequent hysteresis thresholding in the saturation channel of the enhanced images. The second approach seems to be more viable because of its robustness and more promising results

Adaptive color spaces based on multivariate Gaussian distributions for color image segmentation

We formulate an adaptive color space for segmenting all image into the two classes "object of interest" and "background" by using well-established methods from statistical pattern recognition. Both classes are modeled by a multivariate Gaussian distribution whose actual parameters are estimated via the Expectation Maximization (EM) algorithm. The output grayscale feature image is derived as the distance of each pixel's color to the decision boundary which is shaped bewteen the two class models. Based on this feature image, which provides a maximum discriminatory power with respect to the underlying model assumptions, the actual segmentation can be performed with appropriate methods from grayscale image processing. This adaptive color space is a practical tool for homogeneously colored scenes, as they appear, e.g., in microscopic images of biotechnical fundamental research