Intelligent GPGPU Classification in Volume Visualization: A framework based on Error‐Correcting Output Codes

Abstract

In volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on-demand multiple regions of interest. These methods can be split into a two-level GPU-based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error-Correcting Output Codes (ECOC) framework. In a pre-processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre-labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi-class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU-OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy.

Spatial codification of label predictions in multi‐scale stacked sequential learning: a case study on multi‐class medical volume segmentation

“…For both cases, the voxel feature set includes the voxel 3D coordinates, intensity, gradient magnitude and gradient along each of the three dimensions, defining an initial feature set for each voxel of eight features. These datasets have been used, among others, to test classification performance of different state‐of‐the art classifiers (such as AdaBoost [12]) in volume segmentation scenarios [18].…”

“…Basically, in this context the data entries x are the voxel's features (with W its associated codeword) and the possible classification labels correspond to the voxel's belonging to the different segmentation regions. In [18], the authors shown that using the Hamming distance (HD) metric [17] as the ECOC decoding measure has proven successful results in volume segmentation scenarios (Fig. 6).…”