Joint Nonparametric Alignment for Analyzing Spatial Gene Expression Patterns in Drosophila Imaginal Discs

To compare spatial patterns of gene expression, one must analyze a large number of images as current methods are only able to measure a small number of genes at a time. Bringing images of corresponding tissues into alignment is a critical first step in making a meaningful comparative analysis of these spatial patterns. Significant image noise and variability in the shapes make it hard to pick a canonical shape model. In this paper, we address these problems by combining segmentation and unsupervised shape learning algorithms. We first segment images to acquire structures of interest, then jointly align the shapes of these acquired structures using an unsupervised nonparametric maximum likelihood algorithm along the lines of `congealing' [12], while simultaneously learning the underlying shape model and associated transformations. The learned transformations are applied to corresponding images to bring them into alignment in one step. We demonstrate the results for images of vario...

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Parvez Ahammad, Cyrus L. Harmon, Ann Hammonds, Shankar Sastry, Gerald M. Rubin
• Keywords: Canonical Shape Model, Computer Vision, CVPR 2005, Shape Learning Algorithms, Spatial Gene Expression, Spatial Patterns, Unsupervised Nonparametric Maximum