Contour Extraction of Drosophila Embryos

Abstract

Contour extraction of Drosophila (fruit fly) embryos is an important step to build a computational system for matching expression pattern of embryonic images to assist the discovery of the nature of genes. Automatic contour extraction of embryos is challenging due to severe image variations, including 1) the size, orientation, shape, and appearance of an embryo of interest; 2) the neighboring context of an embryo of interest (such as nontouching and touching neighboring embryos); and 3) illumination circumstance. In this paper, we propose an automatic framework for contour extraction of the embryo of interest in an embryonic image. The proposed framework contains three components. Its first component applies a mixture model of quadratic curves, with statistical features, to initialize the contour of the embryo of interest. An efficient method based on imbalanced image points is proposed to compute model parameters. The second component applies active contour model to refine embryo contours. The third component applies eigen-shape modeling to smooth jaggy contours caused by blurred embryo boundaries. We test the proposed framework on a data set of 8,000 embryonic images, and achieve promising accuracy (88 percent), that is, substantially higher than the-state-of-the-art results.

A geometric method for contour extraction of Drosophila embryos

“…The dataset contains 2000 images of BDGP Drosophila embryos. Table 1 shows the quantitative results, including the comparison of two existing methods on contour extraction of Drosophila embryos: i) Li and Kambhamettu’s method [3] that consists an initialization based on a quadratic curve model, and a refinement based on an active contour model; ii) Li’s method [4] that can detect and restore deficiencies and faults of primal sketch tokens occurring when a targeting object is surrounded by a complex background. …”

“…In general, Drosophila embryonic images contain substantial amount of variations [3, 4]: i) imaging conditions, such as contrasts, scale, orientation, and neighboring embryos, ii) gene expression patterns, and iii) developmental stages. Most existing methods were developed upon low-level image features, such as edge pixels or pixels with a high deviation of grayvalues in a local window [3–10].…”

“…Most existing methods were developed upon low-level image features, such as edge pixels or pixels with a high deviation of grayvalues in a local window [3–10]. Peng and Myers [5] proposed a method that uses the standard deviation of the local windows of a pixel to classify the pixel as a foreground or background pixel.…”

“…Mace et al [9] proposed an eigen-embryo method to extract the contour of embryos, where a particle swarm optimizer was used to reduce the computational cost of searching optimal eigen parameters. Li and Kambhamettu [3] proposed a quadratic curve model to initialize the contour of the embryo of interest based on edge pixels, and applied an active contour model to refine embryo contours. Bessinger et al [10] proposed criteria to select the optimal connected component of edge pixels in the scale space of an input image.…”