PCA based SVD fusion for MRI and CT medical images

This paper presents a new approach to the multi-modal medical image fusion based on Principal Component Analysis (PCA) and Singular value decomposition (SVD).The main objective of the proposed approach is to facilitate its implementation on a hardware unit, so it works effectively at run time. To evaluate the presented approach, it was tested in fusing four different cases of a registered CT and MRI images. Eleven quality metrics (including Mutual Information and Universal Image Quality Index) were used in evaluating the fused image obtained by the proposed approach, and compare it with the images obtained by the other fusion approaches. In experiments, the quality metrics shows that the fused image obtained by the presented approach has better quality result and it proved effective in medical image fusion especially in MRI and CT images. It also indicates that the paper approach had reduced the processing time and the memory required during the fusion process, and leads to very cheap and fast hardware implementation of the presented approach.

Statistical properties of an unassisted image quality index for SAR imagery

© 2019 by the authors. The M estimator is a recently proposed image-quality index used to evaluate the despeckling operation in SAR (Synthetic Aperture Radar) data. It is used also to rank despeckling filters and to improve their design. As a difference with traditional image-quality estimators, it operates not on the filtered result but on a derived one, i.e., the ratio image. However, a deep statistical analysis of its properties remains open and, with it, the ability to use it as a test statistic. In this work, we focus on obtaining insights into its distribution as well as on exploring other remarkable statistical properties of this unassisted estimator. This study is performed through EDA (Exploratory Data Analysis) and the well-known ANOVA (ANalysis Of VAriance). We test our results on a set of simulated SAR data and provide guides to enrich theMestimator to extend its capabilities.

Statistical properties of an unassisted image quality index for SAR imagery

© 2019 by the authors. The M estimator is a recently proposed image-quality index used to evaluate the despeckling operation in SAR (Synthetic Aperture Radar) data. It is used also to rank despeckling filters and to improve their design. As a difference with traditional image-quality estimators, it operates not on the filtered result but on a derived one, i.e., the ratio image. However, a deep statistical analysis of its properties remains open and, with it, the ability to use it as a test statistic. In this work, we focus on obtaining insights into its distribution as well as on exploring other remarkable statistical properties of this unassisted estimator. This study is performed through EDA (Exploratory Data Analysis) and the well-known ANOVA (ANalysis Of VAriance). We test our results on a set of simulated SAR data and provide guides to enrich theMestimator to extend its capabilities.

A robust watermarking algorithm for medical images

<p>Integrated healthcare systems require the transmission of medical images between medical centres. The presence of watermarks in such images has become important for patient privacy protection. However, some important issues should be considered while watermarking an image. Among these issues, the watermark should be robust against attacks and does not affect the quality of the image. In this paper, a watermarking approach employing a robust dynamic secret code is proposed. This approach is to process every pixel of the digital image and not only the pixels of the regions of non-interest at the same time it preserves the image details. The performance of the proposed approach is evaluated using several performance measures such as the Mean Square Error (MSE), the Mean Absolute Error (MAE), the Peak Signal to Noise Ratio (PSNR), the Universal Image Quality Index (UIQI) and the Structural Similarity Index (SSIM). The proposed approach has been tested and shown robustness in detecting the intentional attacks that change image, specifically the most important diagnostic information.</p>

Ultrasound semiautomatic versus manual estimation of carotid intima-media thickness: reproducibility and cardiovascular risk stratification

Aims: Carotid intima-media thickness (CIMT) is used increasingly as an imaging biomarker of cardiovascular risk (CVR). Our aim was to compare semiautomatic CIMT (sCIMT) versus manual CIMT (mCIMT) for reproducibility and prediction of CVR.Materials and methods: Two independent readers measured sCIMT and mCIMT on previously acquired images of the right common carotid artery of 200 consecutive patients. Measurements were performed twice, four weeks apart; sCIMT was reported along with an image quality index (IQI) provided by the software. CVR stratification was compared for thresholds established by mCIMT studies, adapted for sCIMT according to a regression model.Results: sCIMT (median 0.67 mm, interquartile range [IQR] 0.57‒0.76 mm) was significantly lower (p<0.001) than mCIMT (median 0.76 mm, IQR 0.63‒0.84 mm; ρ=0.832, p<0.001, slope 0.714, intercept 0.124). Overall, intra-reader reproducibility was 76% for sCIMT and 83% for mCIMT (p=0.002), inter-reader reproducibility 75% and 76%, respectively (p=0.316). In 129 cases with IQI≥0.65, reproducibility was significantly higher (p≤0.004) for sCIMT than for mCIMT (intra-reader 85% versus 83%, inter-reader 80% versus 77%,). The agreement between sCIMT and mCIMT for CVR stratification was fair both overall (κ=0.270) and for IQI≥0.65 (κ=0.345), crude concordance being 79% and 88%, respectively.Conclusions: Reproducibility of sCIMT was not higher than mCIMT overall but sCIMT was significantly more reproducible than mCIMT for high-IQI cases. sCIMT cannot be used for CVR stratification due to fair concordance with mCIMT, even for high IQI. More research is required to improve image quality and define sCIMT-based thresholds for stratification of CVR.

Static Thresholded Pulse Coupled Neural Networks in Contourlet Domain — A New Framework for Medical Image Denoising

Image denoising, a significant research area in the field of medical image processing, makes an effort to recover the original image from its noise corrupted image. The Pulse Coupled Neural Networks (PCNN) works well against denoising a noisy image. Generally, image denoising techniques are directly applied on the pixels. From the literature review, it is reported that denoising after frequency domain transformation is performing better since noise removal is applied over the coefficients. Motivated by this, in this paper, a new technique called the Static Thresholded Pulse Coupled Neural Network (ST-PCNN) is proposed by combining PCNN with traditional filtering or threshold shrinkage technique in Contourlet Transform domain. Four different existing PCNN architectures, such as Neuromime Structure, Intersecting Cortical Model, Unit-Linking Model and Multichannel Model are considered for comparative analysis. The filters such as Wiener, Median, Average, Gaussian and threshold shrinkage techniques such as Sure Shrink, HeurShrink, Neigh Shrink, BayesShrink are used. For noise removal, a mixture of Speckle and Gaussian noise is considered for a CT skull image. A mixture of Rician and Gaussian noise is considered for MRI brain image. A mixture of Speckle and Salt and Pepper noise is considered for a Mammogram image. The Performance Metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Image Quality Index (IQI), Universal Image Quality Index (UQI), Image Enhancement Filter (IEF), Structural Content (SC), Correlation Coefficient (CC), and Weighted Signal-to-Noise Ratio (WSNR) and Visual Signal-to-Noise Ratio (VSNR) are used to evaluate the performance of denoising.