Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation

This paper proposes to generate and to use barcodes to annotate medical images and/or their regions of interest such as organs, tumors and tissue types. A multitude of efficient feature-based image retrieval methods already exist that can assign a query image to a certain image class. Visual annotations may help to increase the retrieval accuracy if combined with existing feature-based classification paradigms. Whereas with annotations we usually mean textual descriptions, in this paper barcode annotations are proposed. In particular, Radon barcodes (RBC) are introduced. As well, local binary patterns (LBP) and local Radon binary patterns (LRBP) are implemented as barcodes. The IRMA x-ray dataset with 12,677 training images and 1,733 test images is used to verify how barcodes could facilitate image retrieval.Comment: To be published in proceedings of The IEEE International Conference on Image Processing (ICIP 2015), September 27-30, 2015, Quebec City, Canad

MinMax Radon Barcodes for Medical Image Retrieval

Content-based medical image retrieval can support diagnostic decisions by clinical experts. Examining similar images may provide clues to the expert to remove uncertainties in his/her final diagnosis. Beyond conventional feature descriptors, binary features in different ways have been recently proposed to encode the image content. A recent proposal is "Radon barcodes" that employ binarized Radon projections to tag/annotate medical images with content-based binary vectors, called barcodes. In this paper, MinMax Radon barcodes are introduced which are superior to "local thresholding" scheme suggested in the literature. Using IRMA dataset with 14,410 x-ray images from 193 different classes, the advantage of using MinMax Radon barcodes over \emph{thresholded} Radon barcodes are demonstrated. The retrieval error for direct search drops by more than 15\%. As well, SURF, as a well-established non-binary approach, and BRISK, as a recent binary method are examined to compare their results with MinMax Radon barcodes when retrieving images from IRMA dataset. The results demonstrate that MinMax Radon barcodes are faster and more accurate when applied on IRMA images.Comment: To appear in proceedings of the 12th International Symposium on Visual Computing, December 12-14, 2016, Las Vegas, Nevada, US

Indexing of Shape Images based on Complementary Composited Features

Abstract: Till now systems exploit spatial features. None of the available systems combines all features, texture, and shape for retrieval. This paper proposes a simple Object-Based Retrieval (OBR) systems, which a novel feature-based shapes descriptors using Radon composite features by using statistical and spectral analysis are used in this system, Instead of analyzing shapes directly in the spatial domain. Moreover relatively few systems use Transform in texture extraction features, despite the widely acclaimed efficiency. The proposed system uses combination of radon transformed image features, and Moments features of the regions as shape features then kernel Linear Discriminant Analysis (KLDA) are applied for decreasing the dimension of feature vector and non none-linear combination of vector dimensions for generating optimum features. Experiments demonstrate that proposed novel feature-based shapes system provides a higher degree of retrieval and are compared with several state-of-the-art approaches

Radon Projections as Image Descriptors for Content-Based Retrieval of Medical Images

Clinical analysis and medical diagnosis of diverse diseases adopt medical imaging techniques to empower specialists to perform their tasks by visualizing internal body organs and tissues for classifying and treating diseases at an early stage. Content-Based Image Retrieval (CBIR) systems are a set of computer vision techniques to retrieve similar images from a large database based on proper image representations. Particularly in radiology and histopathology, CBIR is a promising approach to effectively screen, understand, and retrieve images with similar level of semantic descriptions from a database of previously diagnosed cases to provide physicians with reliable assistance for diagnosis, treatment planning and research. Over the past decade, the development of CBIR systems in medical imaging has expedited due to the increase in digitized modalities, an increase in computational efficiency (e.g., availability of GPUs), and progress in algorithm development in computer vision and artificial intelligence. Hence, medical specialists may use CBIR prototypes to query similar cases from a large image database based solely on the image content (and no text). Understanding the semantics of an image requires an expressive descriptor that has the ability to capture and to represent unique and invariant features of an image. Radon transform, one of the oldest techniques widely used in medical imaging, can capture the shape of organs in form of a one-dimensional histogram by projecting parallel rays through a two-dimensional object of concern at a specific angle. In this work, the Radon transform is re-designed to (i) extract features and (ii) generate a descriptor for content-based retrieval of medical images. Radon transform is applied to feed a deep neural network instead of raw images in order to improve the generalization of the network. Specifically, the framework is composed of providing Radon projections of an image to a deep autoencoder, from which the deepest layer is isolated and fed into a multi-layer perceptron for classification. This approach enables the network to (a) train much faster as the Radon projections are computationally inexpensive compared to raw input images, and (b) perform more accurately as Radon projections can make more pronounced and salient features to the network compared to raw images. This framework is validated on a publicly available radiography data set called "Image Retrieval in Medical Applications" (IRMA), consisting of 12,677 train and 1,733 test images, for which an classification accuracy of approximately 82% is achieved, outperforming all autoencoder strategies reported on the Image Retrieval in Medical Applications (IRMA) dataset. The classification accuracy is calculated by dividing the total IRMA error, a calculation outlined by the authors of the data set, with the total number of test images. Finally, a compact handcrafted image descriptor based on Radon transform was designed in this work that is called "Forming Local Intersections of Projections" (FLIP). The FLIP descriptor has been designed, through numerous experiments, for representing histopathology images. The FLIP descriptor is based on Radon transform wherein parallel projections are applied in a local 3x3 neighborhoods with 2 pixel overlap of gray-level images (staining of histopathology images is ignored). Using four equidistant projection directions in each window, the characteristics of the neighborhood is quantified by taking an element-wise minimum between each adjacent projection in each window. Thereafter, the FLIP histogram (descriptor) for each image is constructed. A multi-resolution FLIP (mFLIP) scheme is also proposed which is observed to outperform many state-of-the-art methods, among others deep features, when applied on the histopathology data set KIMIA Path24. Experiments show a total classification accuracy of approximately 72% using SVM classification, which surpasses the current benchmark of approximately 66% on the KIMIA Path24 data set

Un descripteur efficace pour la reconnaissance des symboles graphiques basé sur la transformée de Radon

ISBN : 978-9973-37-582-7National audienceLe choix des descripteurs constitue un problème majeur dans les systèmes d'analyse d'images, car ces descripteurs conditionnent fortement le résultat final de la recherche ou de la classification. Dans cet article, après avoir proposé un nouveau descripteur invariant aux transformations géométriques usuelles, basé sur la transformée de Radon appelé phi-signature, un autre ensemble de descripteurs qui découle des transformations en ondelettes de ladite signature et de la R-signature, est présenté. Les résultats expérimentaux montrent l'efficacité de ces descripteurs, particulièrement pour des formes complexes non pleines de type symbole graphique

An empirical study of shape recognition in ensemble learning context

Shape recognition has been a popular application of machine learning, where each shape is defined as a class for training classifiers that recognize the shapes of new instances. Since training of classifiers is essentially achieved through learning from features, it is crucial to extract and select a set of relevant features that can effectively distinguish one class from other classes. However, different instances could present features which are highly dissimilar, even if these instances belong to the same class. The above difference in feature representation can also result in high diversity among classifiers trained by using different algorithms or data samples. In this paper, we investigate the impact of multi-classifier fusion on shape recognition by using six features extracted from a 2D shape data set. In particular, popular single learning algorithms, such as Decision Trees, Support Vector Machine and K Nearest Neighbours, are adopted to train base classifiers on features selected by using a wrapper approach. Furthermore, two popular ensemble learning algorithms (Random Forests and Gradient Boosted Trees) are adopted to train decision tree ensembles on the same feature sets. The outputs of the two ensemble classifiers are finally combined with the outputs of all the other base classifiers The experimental results show the effectiveness of the above setting of multi-classifier fusion for advancing the performance in comparison with using each single (non-ensemble) learning algorithm

An algebraic reconstruction technique (ART) for the synthesis of three-dimensional models of particle aggregates from projective representations

There exists considerable evidence that the shear behavior and flow behavior of granular materials is significantly dependent on particle morphology. However, quantification of this dependence is a challenging task owing to a dearth of quantitative models for describing particle shape and the difficulty of modeling angular particle assemblies. The situation becomes more complex when discrete element analyses of realistic 3-D particle shapes are required. The thesis attempts to address this problem by adapting the algebraic reconstruction technique (ART) to synthesize composite 3-D granular particles from statistically obtained 3-D shape descriptors of the particles in an aggregate mixture. This thesis extends previous work where it was demonstrated that the 3-D shape characteristics of particles in an aggregate mixture can be numerically expressed by statistical models obtained from 2-D projective representations of multiple particles in the mixture. In this thesis, attempts were made to validate the premise that multiple projective representations of multiple particles could be used to synthesize a composite 3-D particle that represents the entire mixture in terms of its 3-D shape descriptors. Also, single particles isolated from the aggregate mix were scanned using optical and X-ray tomography techniques to generate 2-D multiple projections and synthesize the 3-D particle shape. This research work proves useful for generating realistic shapes for discrete element applications or in obtaining more fundamental understanding of the micromechanics of granular solids

An empirical study of shape recognition in ensemble learning context

Shape recognition has been a popular application of machine learning, where each shape is defined as a class for training classifiers that recognize the shapes of new instances. Since training of classifiers is essentially achieved through learning from features, it is crucial to extract and select a set of relevant features that can effectively distinguish one class from other classes. However, different instances could present features which are highly dissimilar, even if these instances belong to the same class. The above difference in feature representation can also result in high diversity among classifiers trained by using different algorithms or data samples. In this paper, we investigate the impact of multi-classifier fusion on shape recognition by using six features extracted from a 2D shape data set. In particular, popular single learning algorithms, such as Decision Trees, Support Vector Machine and K Nearest Neighbours, are adopted to train base classifiers on features selected by using a wrapper approach. Furthermore, two popular ensemble learning algorithms (Random Forests and Gradient Boosted Trees) are adopted to train decision tree ensembles on the same feature sets. The outputs of the two ensemble classifiers are finally combined with the outputs of all the other base classifiers The experimental results show the effectiveness of the above setting of multi-classifier fusion for advancing the performance in comparison with using each single (non-ensemble) learning algorithm

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition