Optimization of Joint Detector for Ultrasound Images Using Mixtures of Image Feature Descriptors

Abstract

Joint detector is an essential part of an approach towards automated assessment of synovitis activity, which is a subject of the current research work. A recent formulation of the joint detector, that integrates image processing, local image neighborhood descriptors, such as SURF, FAST, ORB, BRISK, FREAK, trainable classification (SVM, NN, CART) and clusterization, results in a large number of possible choices of classifiers, their modes, components of features vectors, and parameter values, and making such choices by experimentation is impractical. This article presents a novel approach, and an implemented environment for the parameter selection process for the joint detector, which automatically choses the best configuration of image processing operators, type of image neighborhood descriptors, the form of a classifier and the clustering method and their parameters. Its implementation uses new scripting tools and generic techniques, such as chain-of-responsibility design pattern and metafunction idiom. Also presented are novel results, comparing the effect of feature vectors composed from multiple SURF descriptors on the performance of the joint detector, which demonstrate the potential of mixture of descriptors for improving the classification results.