A dual adaptive watermarking scheme in contourlet domain for DICOM images

<p>Abstract</p> <p>Background</p> <p>Nowadays, medical imaging equipments produce digital form of medical images. In a modern health care environment, new systems such as PACS (picture archiving and communication systems), use the digital form of medical image too. The digital form of medical images has lots of advantages over its analog form such as ease in storage and transmission. Medical images in digital form must be stored in a secured environment to preserve patient privacy. It is also important to detect modifications on the image. These objectives are obtained by watermarking in medical image.</p> <p>Methods</p> <p>In this paper, we present a dual and oblivious (blind) watermarking scheme in the contourlet domain. Because of importance of ROI (region of interest) in interpretation by medical doctors rather than RONI (region of non-interest), we propose an adaptive dual watermarking scheme with different embedding strength in ROI and RONI. We embed watermark bits in singular value vectors of the embedded blocks within lowpass subband in contourlet domain.</p> <p>Results</p> <p>The values of PSNR (peak signal-to-noise ratio) and SSIM (structural similarity measure) index of ROI for proposed DICOM (digital imaging and communications in medicine) images in this paper are respectively larger than 64 and 0.997. These values confirm that our algorithm has good transparency. Because of different embedding strength, BER (bit error rate) values of signature watermark are less than BER values of caption watermark. Our results show that watermarked images in contourlet domain have greater robustness against attacks than wavelet domain. In addition, the qualitative analysis of our method shows it has good invisibility.</p> <p>Conclusions</p> <p>The proposed contourlet-based watermarking algorithm in this paper uses an automatically selection for ROI and embeds the watermark in the singular values of contourlet subbands that makes the algorithm more efficient, and robust against noise attacks than other transform domains. The embedded watermark bits can be extracted without the original image, the proposed method has high PSNR and SSIM, and the watermarked image has high transparency and can still conform to the DICOM format.</p

Optimum design of artificial hip joints

This paper describes the biomechanics and designing of the hip joint implants, proposes the ideal requirements of a successful hip joint and studies the current existing artificial hip joint designs on the market where it evaluates the best of those products. Regarding to biomechanics of an artificial hip joint it states the forces applied on the joint and lists various hip joint motions. The statistics of artificial hip joint in UK in terms of type of patients, products, procedures and complications have been cited. . Finally the paper reviews the optimisation process with the aid of FEA technique and specifies the main objectives and progress of this project

Finite element simulation of the hip joint

Rabbani, M and Saidpour, H (2011). ‘Finite element simulation of the hip joint’ Advances on Computing and Technology 6th Annual Conference, University of East London, 116-127

Extraction of Nucleolus Candidate Zone in White Blood Cells of Peripheral Blood Smear Images Using Curvelet Transform

The main part of each white blood cell (WBC) is its nucleus which contains chromosomes. Although white blood cells (WBCs) with giant nuclei are the main symptom of leukemia, they are not sufficient to prove this disease and other symptoms must be investigated. For example another important symptom of leukemia is the existence of nucleolus in nucleus. The nucleus contains chromatin and a structure called the nucleolus. Chromatin is DNA in its active form while nucleolus is composed of protein and RNA, which are usually inactive. In this paper, to diagnose this symptom and in order to discriminate between nucleoli and chromatins, we employ curvelet transform, which is a multiresolution transform for detecting 2D singularities in images. For this reason, at first nuclei are extracted by means of K-means method, then curvelet transform is applied on extracted nuclei and the coefficients are modified, and finally reconstructed image is used to extract the candidate locations of chromatins and nucleoli. This method is applied on 100 microscopic images and succeeds with specificity of 80.2% and sensitivity of 84.3% to detect the nucleolus candidate zone. After nucleolus candidate zone detection, new features that can be used to classify atypical and blast cells such as gradient of saturation channel are extracted

Circuit delay optimization by buffering the logic gates

Avec la miniaturisation actuelle, les circuits démontrent de plus en plus l'importance des délais d'interconnexion. Afin de réduire ce délai, l'insertion de tampons doit être effectuée durant la synthèse logique et la synthèse physique. Cette activité d'optimisation est souvent basée sur la programmation dynamique. Dans ce mémoire, la technique branch-and-bound est utilisé et le problème pour le cas spécifique d'arbres de tampons équilibrés est résolu, où toutes les charges ont un temps requis et une capacité identique. Une analyse mathématique est faite pour tenir compte d'une variété de questions de conception telles que la topologie, la bibliothèque de tampons et le changement de phase en présence d'inverseur. En combinant la programmation dynamique et les techniques branch-and-bound, une méthode hybride est présentée qui améliore le temps d'exécution tout en conservant une utilisation de mémoire raisonnable. Les concepts mathématiques et algorithmiques fondamentaux utilisés dans ce mémoire peuvent être employés pour généraliser la méthode proposée pour un ensemble de charges avec des capacités et des temps requis différents

Dual-Tree Complex Wavelet Input Transform for Cyst Segmentation in OCT Images Based on a Deep Learning Framework

Optical coherence tomography (OCT) represents a non-invasive, high-resolution cross-sectional imaging modality. Macular edema is the swelling of the macular region. Segmentation of fluid or cyst regions in OCT images is essential, to provide useful information for clinicians and prevent visual impairment. However, manual segmentation of fluid regions is a time-consuming and subjective procedure. Traditional and off-the-shelf deep learning methods fail to extract the exact location of the boundaries under complicated conditions, such as with high noise levels and blurred edges. Therefore, developing a tailored automatic image segmentation method that exhibits good numerical and visual performance is essential for clinical application. The dual-tree complex wavelet transform (DTCWT) can extract rich information from different orientations of image boundaries and extract details that improve OCT fluid semantic segmentation results in difficult conditions. This paper presents a comparative study of using DTCWT subbands in the segmentation of fluids. To the best of our knowledge, no previous studies have focused on the various combinations of wavelet transforms and the role of each subband in OCT cyst segmentation. In this paper, we propose a semantic segmentation composite architecture based on a novel U-net and information from DTCWT subbands. We compare different combination schemes, to take advantage of hidden information in the subbands, and demonstrate the performance of the methods under original and noise-added conditions. Dice score, Jaccard index, and qualitative results are used to assess the performance of the subbands. The combination of subbands yielded high Dice and Jaccard values, outperforming the other methods, especially in the presence of a high level of noise

A new convolutional neural network based on combination of circlets and wavelets for macular OCT classification

Artificial intelligence (AI) algorithms, encompassing machine learning and deep learning, can assist ophthalmologists in early detection of various ocular abnormalities through the analysis of retinal optical coherence tomography (OCT) images. Despite considerable progress in these algorithms, several limitations persist in medical imaging fields, where a lack of data is a common issue. Accordingly, specific image processing techniques, such as time–frequency transforms, can be employed in conjunction with AI algorithms to enhance diagnostic accuracy. This research investigates the influence of non-data-adaptive time–frequency transforms, specifically X-lets, on the classification of OCT B-scans. For this purpose, each B-scan was transformed using every considered X-let individually, and all the sub-bands were utilized as the input for a designed 2D Convolutional Neural Network (CNN) to extract optimal features, which were subsequently fed to the classifiers. Evaluating per-class accuracy shows that the use of the 2D Discrete Wavelet Transform (2D-DWT) yields superior outcomes for normal cases, whereas the circlet transform outperforms other X-lets for abnormal cases characterized by circles in their retinal structure (due to the accumulation of fluid). As a result, we propose a novel transform named CircWave by concatenating all sub-bands from the 2D-DWT and the circlet transform. The objective is to enhance the per-class accuracy of both normal and abnormal cases simultaneously. Our findings show that classification results based on the CircWave transform outperform those derived from original images or any individual transform. Furthermore, Grad-CAM class activation visualization for B-scans reconstructed from CircWave sub-bands highlights a greater emphasis on circular formations in abnormal cases and straight lines in normal cases, in contrast to the focus on irrelevant regions in original B-scans. To assess the generalizability of our method, we applied it to another dataset obtained from a different imaging system. We achieved promising accuracies of 94.5% and 90% for the first and second datasets, respectively, which are comparable with results from previous studies. The proposed CNN based on CircWave sub-bands (i.e. CircWaveNet) not only produces superior outcomes but also offers more interpretable results with a heightened focus on features crucial for ophthalmologists