On the Segmentation and Classification of Hand Radiographs

Abstract

This research is part of a wider project to build predictive models of bone age using hand radiograph images. We examine ways of finding the outline of a hand from an X-ray as the first stage in segmenting the image into constituent bones. We assess a variety of algorithms including contouring, which has not previously been used in this context. We introduce a novel ensemble algorithm for combining outlines using two voting schemes, a likelihood ratio test and dynamic time warping (DTW). Our goal is to minimize the human intervention required, hence we investigate alternative ways of training a classifier to determine whether an outline is in fact correct or not. We evaluate outlining and classification on a set of 1370 images. We conclude that ensembling with DTW improves performance of all outlining algorithms, that the contouring algorithm used with the DTW ensemble performs the best of those assessed, and that the most effective classifier of hand outlines assessed is a random forest applied to outlines transformed into principal components.

Euler State Networks: Non-dissipative Reservoir Computing

“…We assessed the performance of EuSN on several real-world time-series classification benchmarks of diverse nature. The first 10 datasets were taken from the UEA & UCR time-series classification repository [45], namely: Adiac [46], CharacterTrajectories [47], ECG5000 [48], Epilepsy [49], HandOutlines [50], Hearthbeat [48], Libras [51], ShapesAll [52], SpokenArabicDigits [53], and Wafer [54]. Moreover, we have considered the IMDB movie review sentiment classification dataset [55], and the Reuters newswire classification dataset from UCI [56].…”

Time series ordinal classification via shapelets

“…Given that this is the first time that time series ordinal classification is studied, a subset of 7 time series datasets has been appropriately chosen from the original UCR data repository [28]. Most of the datasets selected come from the field of bone age prediction, presented in [29]. Specifically, those named "AgeGroup" include patterns (bones) labelled as inf ant, junior or teen, depending on the age group to which the bone belongs.…”

A Genetic Graph-Based Approach for Partitional Clustering

“…2 The most recent approaches combine classification techniques with clustering algorithms to improve the results quality, for example, Hsu uses neural networks applied to brain-computer interface systems, 21 Kodogiannis et al use neural networks and fuzzy clustering for short-term load forecasting 22 and Davis et al combine segmentation and classification for hand radiography. 23 Other research lines have tried to improve these algorithms. For example, some online methods have been developed to avoid the K-means convergence problem to local solutions which depend on the initial values.…”