Feature extraction of chromosomes from 3D confocal microscope images

Abstract

Use of a confocal light microscope enables biologists to observe dividing cells (living or presented) within a 3D volume that can be visualised from multiple aspects. The Nomarski differential interference contrast (DIG) mode used for imaging translucent specimens, such as chromosomes, produces images not suitable for volume rendering. Segmentation of the chromosomes from this data is thus necessary. Kohonen's self-organising feature map (SOFM) was used to perform segmentation, based on a collection of various statistics or features defining the image. In the past, classical features such as the mean and variance of pixel intensities have been used, providing reasonable extraction of chromosome bodies, while only mildly resolving surface detail. In this investigation, a local energy feature detector was implemented, producing an alternative image statistic based on phase congruency in the image. This, along with combinations of other image statistics, was applied to the SOFM, producing a series of resultant 3D images exhibiting vast improvements in the level of detail defining the internal structure of the specimen chromosomes.