|Title||Average cell orientation, shape and size estimated from tissue images|
|Publication Type||Conference Paper|
|Year of Publication||2005|
|Authors||Iles, P., D. A. Clausi, S. Puddister, and G. W. Brodland|
|Conference Name||2nd Annual Canadian Conference on Computer and Robot Vision|
|Conference Location||Victoria, B.C., Canada|
|Keywords||area moments, average cell orientation, average cell shape, average cell size, biological tissue, biological tissues, biology computing, biomechanical tissue models, cell boundary visibility, cell geometry, cell pigmentation, Computer Vision, edge detection, elliptical composite cell, embryology, Gabor filters, least squares ellipse fitting, magnitude spatial-frequency domain, medical image processing, morphogenesis, shape detection, simple edge detection, statistical analysis, tissue images|
Four computer vision algorithms to measure the average orientation, shape and size of cells in images of biological tissue are proposed and tested. These properties, which can be embodied by an elliptical 'composite cell' are crucial for biomechanical tissue models. To automatically determine these properties is challenging due to the diverse nature of the image data, with tremendous and unpredictable variability in illumination, cell pigmentation, cell shape, and cell boundary visibility. First, a simple edge detection routine is performed on the raw images to locate cell edges and remove pigmentation variation. The edge map is then converted into the magnitude spatial-frequency domain where the spatial patterns of the cells appear as energy impulses. Four candidate methods that analyze the spatial-frequency data to estimate the properties of the composite cell are presented and compared. These methods are: least squares ellipse fitting, correlation, area moments and Gabor filters. Robustness is demonstrated by successful application on a wide variety of real images.