Distributed Connected Component Filtering and Analysis in 2D and 3D Tera-Scale Data Sets.

Abstract

Connected filters and multi-scale tools are region-based operators acting on the connected components of an image. Component trees are image representations to efficiently perform these operations as they represent the inclusion relationship of the connected components hierarchically. This paper presents disccofan (DIStributed Connected COmponent Filtering and ANalysis), a new method that extends the previous 2D implementation of the Distributed Component Forests (DCFs) to handle 3D processing and higher dynamic range data sets. disccofan combines shared and distributed memory techniques to efficiently compute component trees, user-defined attributes filters, and multi-scale analysis. Compared to similar methods, disccofan is faster and scales better on low and moderate dynamic range images, and is the only method with a speed-up larger than 1 on a realistic, astronomical floating-point data set. It achieves a speed-up of 11.20 using 48 processes to compute the DCF of a 162 Gigapixels, single-precision floating-point 3D data set, while reducing the memory used by a factor of 22. This approach is suitable to perform attribute filtering and multi-scale analysis on very large 2D and 3D data sets, up to single-precision floating-point value.