Feature extraction of chromosomes from 3-D confocal microscope images

An investigation of local energy surface detection integrated with neural n etwork techniques for image segmentation is presented, as applied in the fe ature extraction of chromosomes from image datasets obtained using an exper imental confocal microscope. Use of the confocal microscope enables biologi sts to observe dividing cells (living or preserved) within a three-dimensio nal (3-D) volume, that can be visualised from multiple aspects, allowing fo r increased structural insight. The Nomarski differential interference cont rast mode used for imaging translucent specimens, such as chromosomes, prod uces images not suitable for volume rendering. Segmentation of the chromoso mes from this data is,. thus, necessary. A neural network based on competitive learning, known as Kohonen's self-org anizing feature map (SOFM) was used to perform segmentation, using a collec tion of statistics or features defining the image. Our past investigation s howed that standard features such as the localized mean and variance of pix el intensities provided reasonable extraction of objects such as mitotic ch romosomes, but surface detail was only moderately resolved. In this current work, a biologically inspired feature known as local energy is investigate d as an alternative image statistic based on phase congruency in the image. This, along with different combinations of other image statistics, is appl ied in a SOFM, producing 3-D images exhibiting vast improvement in the leve l of detail and clearly isolating the chromosomes from the background.