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Published in:
2015 | OriginalPaper | Chapter
Guided Random Forests for Identification of Key Fetal Anatomy and Image Categorization in Ultrasound Scans
Authors : Mohammad Yaqub, Brenda Kelly, A. T. Papageorghiou, J. Alison Noble
Published in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015
Publisher: Springer International Publishing
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In this paper, we propose a novel machine learning based method to categorize unlabeled fetal ultrasound images. The proposed method guides the learning of a Random Forests classifier to extract features from regions inside the images where meaningful structures exist. The new method utilizes a translation and orientation invariant feature which captures the appearance of a region at multiple spatial resolutions. Evaluated on a large real world clinical dataset (~30K images from a hospital database), our method showed very promising categorization accuracy (accuracy
top1
is 75% while accuracy
top2
is 91%).