Contour extraction of Drosophila embryos is an important step to build a computational system for pattern matching of embryonic images which aids in the discovery of genes. Automatic contour extraction of embryos is challenging due to several image variations such as size, shape, orientation and neigh- boring embryos such as touching and non-touching embryos. In this thesis, we introduce a framework for contour extraction based on the connected components in the gaussian scale space of an embryonic image. The active contour model is applied on the images to refine embryo contours. Data cleaning methods are applied to smooth the jaggy contours caused by blurred embryo boundaries. The scale space theory is applied to improve the performance of the result. The active contour adjusts better to the object for finer scales. The proposed framework contains three components. In the first component, we find the connected components of the image. The second component is to find the largest component of the image. Finally, we analyze the largest component across scales by selecting the optimal scale corresponding to the largest component having largest area. The optimal scale at which maximum area is attained is assumed to give information about the feature being extracted. We tested the proposed framework on BDGP images, and the results achieved promising accuracy in extracting the targeting embryo.