Random Forest Regression Voting Research Articles

Multi-point Regression Voting for Shape Model Matching

Regression-based schemes have proven effective for locating landmarks on images. Most previous approaches either predict the positions of all points simultaneously, or use regressors that predict individual points combined with a global shape constraint. The former can be efficient, but such models tend to be less robust. Conversely, Random Forest (RF) voting methods for individual points have been shown to be robust and accurate, but can lead to very large models. We explore the continuum between these two approaches by training RF regressors to predict subsets of points.Multi-point regression voting was implemented within the Random Forest Regression Voting Constrained Local Model frame- work and evaluated on clinical and facial images. Significant model size reductions were achieved with little difference in accuracy. The approach may therefore be useful where high numbers of points, and limitations on memory or disk space, make single-point models impractically large.

Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting.

A widely used approach for locating points on deformable objects in images is to generate feature response images for each point, and then to fit a shape model to these response images. We demonstrate that Random Forest regression-voting can be used to generate high quality response images quickly. Rather than using a generative or a discriminative model to evaluate each pixel, a regressor is used to cast votes for the optimal position of each point. We show that this leads to fast and accurate shape model matching when applied in the Constrained Local Model framework. We evaluate the technique in detail, and compare it with a range of commonly used alternatives across application areas: the annotation of the joints of the hands in radiographs and the detection of feature points in facial images. We show that our approach outperforms alternative techniques, achieving what we believe to be the most accurate results yet published for hand joint annotation and state-of-the-art performance for facial feature point detection.

Localization of vertebrae on DXA VFA images using constrained local models with random forest regression voting

Fractures associated with osteoporosis are a significant public health risk, and one that is likely to increase with an ageing population. However, many osteoporotic vertebral fractures present on images do not come to clinical attention or lead to preventative treatment. Furthermore, vertebral fracture assessment (VFA) typically depends on subjective judgement by a radiologist. The potential utility of computer-aided VFA systems is therefore considerable. Previous work has shown that Active Appearance Models (AAMs) give accurate results when locating landmarks on vertebra in DXA images, but can give poor fits in a substantial subset of examples, particularly the more severe fractures. Here we evaluate Random Forest Regression Voting Constrained Local Models (RFRV-CLMs) for this task and show that, while they lead to slightly poorer median errors than AAMs, they are much more robust, reducing the proportion of fit failures by 68 %. They are thus more suitable for use in computer-aided VFA systems.

Fully Automatic Segmentation of the Proximal Femur Using Random Forest Regression Voting

Extraction of bone contours from radiographs plays an important role in disease diagnosis, preoperative planning, and treatment analysis. We present a fully automatic method to accurately segment the proximal femur in anteroposterior pelvic radiographs. A number of candidate positions are produced by a global search with a detector. Each is then refined using a statistical shape model together with local detectors for each model point. Both global and local models use Random Forest regression to vote for the optimal positions, leading to robust and accurate results. The performance of the system is evaluated using a set of 839 images of mixed quality. We show that the local search significantly outperforms a range of alternative matching techniques, and that the fully automated system is able to achieve a mean point-to-curve error of less than 0.9 mm for 99% of all 839 images. To the best of our knowledge, this is the most accurate automatic method for segmenting the proximal femur in radiographs yet reported.

O.259 3D-planning and navigation in CMF surgery

Landmark identification is crucial in quantifying cephalometric analysis. Manual tracing of landmarks is a tedious, laborious task and prone to human errors, thereby, necessitating a need to develop efficient automated methods for landmark identification. The advent of artificial intelligence and machine learning has made the automation of cephalometric landmarking, seemingly possible. Techniques such as active shape modelling, active appearance modelling, random forest regression-voting, Convolutional Neural Network (CNN), fuzzy systems and many others have rendered promising results in this field. This work reviews and critically analyses various techniques used for cephalometric landmark identification. This study also highlights current applications, addresses gaps in literature and presents the open challenges in this field.