KEYWORDS: Binary data, Databases, Image classification, 3D image processing, Magnetic resonance imaging, Medical imaging, Tumors, Analytical research, Tissues, Medical research
Volumetric texture analysis is an import task in medical imaging domain and is widely used for characterizing tissues and tumors in medical volumes. Local binary pattern (LBP) based texture descriptors are quite successful for characterizing texture information in 2D images. Unfortunately, the number of binary patterns grows exponentially with number of bits in LBP. Hence its straightforward extension to 3D domain results in extremely large number of bit patterns that may not be relevant for subsequent tasks like classification. In this work we present an efficient extension of LBP for 3D data using decision tree. The leaves of this tree represent texture words whose binary patterns are encoded using the path being followed from the root to reach the leaf. Once trained, this tree is used to create histogram in bag-of-words fashion that can be used as texture descriptor for whole volumetric image. For training, each voxel is converted into a 3D LBP pattern and is assigned the label of it’s corresponding volumetric image. These patterns are used in supervised fashion to construct decision tree. The leaves of the corresponding tree are used as texture descriptor for downstream learning tasks. The proposed texture descriptor achieved state of the art classification results on RFAI database 1. We further showed its efficacy on MR knee protocol classification task where we obtained near perfect results. The proposed algorithm is extremely efficient, computing texture descriptor of typical MRI image in less than 100 milliseconds.
In this paper we propose a novel approach based on multi-stage random forests to address problems faced by
traditional vessel segmentation algorithms on account of image artifacts such as stitches organ shadows etc.. Our
approach consists of collecting a very large number of training data consisting of positive and negative examples
of valid seed points. The method makes use of a 14x14 window around a putative seed point. For this window
three types of feature vectors are computed viz. vesselness, eigenvalue and a novel effective margin feature. A
random forest RF is trained for each of the feature vectors. At run time the three RFs are applied in succession
to a putative seed point generated by a naiive vessel detection algorithm based on vesselness. Our approach will
prune this set of putative seed points to correctly identify true seed points thereby avoiding false positives. We
demonstrate the effectiveness of our algorithm on a large dataset of angio images.
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