Gabor Barcodes for Medical Image Retrieval

In recent years, advances in medical imaging have led to the emergence of massive databases, containing images from a diverse range of modalities. This has significantly heightened the need for automated annotation of the images on one side, and fast and memory-efficient content-based image retrieval systems on the other side. Binary descriptors have recently gained more attention as a potential vehicle to achieve these goals. One of the recently introduced binary descriptors for tagging of medical images are Radon barcodes (RBCs) that are driven from Radon transform via local thresholding. Gabor transform is also a powerful transform to extract texture-based information. Gabor features have exhibited robustness against rotation, scale, and also photometric disturbances, such as illumination changes and image noise in many applications. This paper introduces Gabor Barcodes (GBCs), as a novel framework for the image annotation. To find the most discriminative GBC for a given query image, the effects of employing Gabor filters with different parameters, i.e., different sets of scales and orientations, are investigated, resulting in different barcode lengths and retrieval performances. The proposed method has been evaluated on the IRMA dataset with 193 classes comprising of 12,677 x-ray images for indexing, and 1,733 x-rays images for testing. A total error score as low as $351$ ($\approx 80\%$ accuracy for the first hit) was achieved.Comment: To appear in proceedings of The 2016 IEEE International Conference on Image Processing (ICIP 2016), Sep 25-28, 2016, Phoenix, Arizona, US

Content-based image retrieval of museum images

Content-based image retrieval (CBIR) is becoming more and more important with the advance of multimedia and imaging technology. Among many retrieval features associated with CBIR, texture retrieval is one of the most difficult. This is mainly because no satisfactory quantitative definition of texture exists at this time, and also because of the complex nature of the texture itself. Another difficult problem in CBIR is query by low-quality images, which means attempts to retrieve images using a poor quality image as a query. Not many content-based retrieval systems have addressed the problem of query by low-quality images. Wavelet analysis is a relatively new and promising tool for signal and image analysis. Its time-scale representation provides both spatial and frequency information, thus giving extra information compared to other image representation schemes. This research aims to address some of the problems of query by texture and query by low quality images by exploiting all the advantages that wavelet analysis has to offer, particularly in the context of museum image collections. A novel query by low-quality images algorithm is presented as a solution to the problem of poor retrieval performance using conventional methods. In the query by texture problem, this thesis provides a comprehensive evaluation on wavelet-based texture method as well as comparison with other techniques. A novel automatic texture segmentation algorithm and an improved block oriented decomposition is proposed for use in query by texture. Finally all the proposed techniques are integrated in a content-based image retrieval application for museum image collections

Automatic region-of-interest extraction in low depth-of-field images

PhD ThesisAutomatic extraction of focused regions from images with low depth-of-field (DOF) is a problem without an efficient solution yet. The capability of extracting focused regions can help to bridge the semantic gap by integrating image regions which are meaningfully relevant and generally do not exhibit uniform visual characteristics. There exist two main difficulties for extracting focused regions from low DOF images using high-frequency based techniques: computational complexity and performance. A novel unsupervised segmentation approach based on ensemble clustering is proposed to extract the focused regions from low DOF images in two stages. The first stage is to cluster image blocks in a joint contrast-energy feature space into three constituent groups. To achieve this, we make use of a normal mixture-based model along with standard expectation-maximization (EM) algorithm at two consecutive levels of block size. To avoid the common problem of local optima experienced in many models, an ensemble EM clustering algorithm is proposed. As a result, relevant blocks, i.e., block-based region-of-interest (ROI), closely conforming to image objects are extracted. In stage two, two different approaches have been developed to extract pixel-based ROI. In the first approach, a binary saliency map is constructed from the relevant blocks at the pixel level, which is based on difference of Gaussian (DOG) and binarization methods. Then, a set of morphological operations is employed to create the pixel-based ROI from the map. Experimental results demonstrate that the proposed approach achieves an average segmentation performance of 91.3% and is computationally 3 times faster than the best existing approach. In the second approach, a minimal graph cut is constructed by using the max-flow method and also by using object/background seeds provided by the ensemble clustering algorithm. Experimental results demonstrate an average segmentation performance of 91.7% and approximately 50% reduction of the average computational time by the proposed colour based approach compared with existing unsupervised approaches

Radon-Gabor Barcodes for Medical Image Retrieval

In recent years, with the explosion of digital images on the Web, content-based retrieval has emerged as a significant research area. Shapes, textures, edges and segments may play a key role in describing the content of an image. Radon and Gabor transforms are both powerful techniques that have been widely studied to extract shape-texture-based information. The combined Radon-Gabor features may be more robust against scale/rotation variations, presence of noise, and illumination changes. The objective of this paper is to harness the potentials of both Gabor and Radon transforms in order to introduce expressive binary features, called barcodes, for image annotation/tagging tasks. We propose two different techniques: Gabor-of-Radon-Image Barcodes (GRIBCs), and Guided-Radon-of-Gabor Barcodes (GRGBCs). For validation, we employ the IRMA x-ray dataset with 193 classes, containing 12,677 training images and 1,733 test images. A total error score as low as 322 and 330 were achieved for GRGBCs and GRIBCs, respectively. This corresponds to $\approx 81\%$ retrieval accuracy for the first hit.Comment: To appear in proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, December 201

Spread spectrum-based video watermarking algorithms for copyright protection

Merged with duplicate record 10026.1/2263 on 14.03.2017 by CS (TIS)Digital technologies know an unprecedented expansion in the last years. The consumer can now benefit from hardware and software which was considered state-of-the-art several years ago. The advantages offered by the digital technologies are major but the same digital technology opens the door for unlimited piracy. Copying an analogue VCR tape was certainly possible and relatively easy, in spite of various forms of protection, but due to the analogue environment, the subsequent copies had an inherent loss in quality. This was a natural way of limiting the multiple copying of a video material. With digital technology, this barrier disappears, being possible to make as many copies as desired, without any loss in quality whatsoever. Digital watermarking is one of the best available tools for fighting this threat. The aim of the present work was to develop a digital watermarking system compliant with the recommendations drawn by the EBU, for video broadcast monitoring. Since the watermark can be inserted in either spatial domain or transform domain, this aspect was investigated and led to the conclusion that wavelet transform is one of the best solutions available. Since watermarking is not an easy task, especially considering the robustness under various attacks several techniques were employed in order to increase the capacity/robustness of the system: spread-spectrum and modulation techniques to cast the watermark, powerful error correction to protect the mark, human visual models to insert a robust mark and to ensure its invisibility. The combination of these methods led to a major improvement, but yet the system wasn't robust to several important geometrical attacks. In order to achieve this last milestone, the system uses two distinct watermarks: a spatial domain reference watermark and the main watermark embedded in the wavelet domain. By using this reference watermark and techniques specific to image registration, the system is able to determine the parameters of the attack and revert it. Once the attack was reverted, the main watermark is recovered. The final result is a high capacity, blind DWr-based video watermarking system, robust to a wide range of attacks.BBC Research & Developmen

Multi Voxel Descriptor for 3D Texture Retrieval

In this paper, we present a new feature descriptors  which exploit voxels for 3D textured retrieval system when models vary either by geometric shape or texture or both. First, we perform pose normalisation to modify arbitrary 3D models  in order to have same orientation. We then map the structure of 3D models into voxels. This purposes to make all the 3D models have the same dimensions. Through this voxels, we can capture information from a number of ways.  First, we build biner voxel histogram and color voxel histogram.  Second, we compute distance from centre voxel into other voxels and generate histogram. Then we also compute fourier transform in spectral space.  For capturing texture feature, we apply voxel tetra pattern. Finally, we merge all features by linear combination. For experiment, we use standard evaluation measures such as Nearest Neighbor (NN), First Tier (FT), Second Tier (ST), Average Dynamic Recall (ADR). Dataset in SHREC 2014  and its evaluation program is used to verify the proposed method. Experiment result show that the proposed method  is more accurate when compared with some methods of state-of-the-art