Kyan M J, Guan L, Arnison M R, Cogswell C J
Signal and Multimedia Processing Group, School of Electrical and & Information Systems Engineering, University of Sydney, NSW, Australia.
IEEE Trans Biomed Eng. 2001 Nov;48(11):1306-18. doi: 10.1109/10.959326.
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.
本文介绍了一种将局部能量表面检测与神经网络技术相结合用于图像分割的研究，并将其应用于从使用实验共聚焦显微镜获得的图像数据集中提取染色体特征。共聚焦显微镜的使用使生物学家能够在三维（3-D）体积内观察分裂细胞（活细胞或保存细胞），可以从多个方面进行可视化，从而增加对结构的了解。用于对诸如染色体等半透明标本进行成像的诺马斯基微分干涉对比模式所产生的图像不适用于体积渲染。因此，有必要从这些数据中分割出染色体。一种基于竞争学习的神经网络，即科霍宁自组织特征映射（SOFM），被用于执行分割，使用定义图像的统计数据或特征集合。我们过去的研究表明，诸如像素强度的局部均值和方差等标准特征能够合理地提取有丝分裂染色体等物体，但表面细节只能得到适度的分辨。在当前这项工作中，研究了一种受生物启发的特征——局部能量，作为基于图像相位一致性的替代图像统计量。将其与其他图像统计量的不同组合应用于SOFM中，生成的三维图像在细节水平上有了极大的改进，并且能清晰地将染色体与背景分离。索引词——微分干涉对比、微分干涉对比、特征提取、特征空间、图像分割、局部能量、莫雷小波、相位一致性、自组织特征映射、SOFM
