Feature extraction of chromosomes from 3-D confocal microscope images

Abstract

Abstract-An investigation of local energy surface detection integrated with neural network techniques for image segmentation is presented, as applied in the feature extraction of chromosomes from image datasets obtained using an experimental confocal microscope. Use of the confocal microscope enables biologists to observe dividing cells (living or preserved) within a three-dimensional (3-D) volume, that can be visualised from multiple aspects, allowing for increased structural insight. The Nomarski differential interference contrast 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.A neural network based on competitive learning, known as Kohonen's self-organizing feature map (SOFM) was used to perform segmentation, using a collection of statistics or features defining the image. Our past investigation showed that standard features such as the localized mean and variance of pixel intensities provided reasonable extraction of objects such as mitotic chromosomes, but surface detail was only moderately resolved. In this current work, a biologically inspired feature known as local energy is investigated as an alternative image statistic based on phase congruency in the image. This, along with different combinations of other image statistics, is applied in a SOFM, producing 3-D images exhibiting vast improvement in the level of detail and clearly isolating the chromosomes from the background.Index Terms-DIC, differential interference contrast, feature extraction, feature space, image segmentation, local energy, Morlet wavelet, phase congruency, self organizing feature map, SOFM.

Automated local bright feature image analysis of nuclear protein distribution identifies changes in tissue phenotype

“…The ability to quantify the spatial distribution of fluorescent bright cellular features has many biological applications ranging from the study of gene expression and protein movement in live cells and the exploration of the structural aspects of cell division to the investigation of the role of nuclear alterations in pathologies (30,31,34,(44)(45)(46)(47)(48). We believe that the LBF analysis, which isolates LBFs, and the radial-LBF analysis, which quantifies the distribution of the bright features, are examples of powerful tools capable of measuring differences in the complex distribution of endogenously expressed nuclear proteins from 3D images acquired following simple immunostaining procedures.…”

Application of Kohonen maps for solving the classification puzzle in AGC kinase protein sequences

“…Self Organizing map (SOM) is highly effective, sophisticated visualization tool for visualizing high dimensional multifarious data with inherent relationships among the various features of the data. These have been successfully exploited in medical and health informatics, in fields as varied as medical image processing (Braccini et al, 1997), disease diagnosis (Juhola et al, 2001), chromosome structural studies (Kyan et al, 2001), gene sequence analysis (Dollhopf et al, 2001), expression analysis (Nikkila et al, 2002), structural recognition of protein families (Andrade et al, 1997) and drug designing (Anzali et al, 1998). In recent past, SOMs have been employed for data exploration in major public health diseases like Diabetes (Quintana et al, 2003;Valkonen et al, 2002), Alzheimer (Sepia et al, 2005) and Glaucoma (Sanjun et al, 2005).…”

Target Detection In Sar Images Based On Sub-Aperture Coherence And Phase Congruency

“…T stands for noise estimation, ε represents a small constant that avoids a zero denominator [12] [13].…”