Speckle Noise Reduction in Medical Ultrasound Images Using Modelling of Shearlet Coefficients as a Nakagami Prior

The diagnosis of UltraSound (US) medical images is affected due to the presence of speckle noise. This noise degrades the diagnostic quality of US images by reducing small details and edges present in the image. This paper presents a novel method based on shearlet coefficients modeling of log-transformed US images. Noise-free log-transformed coefficients are modeled as Nakagami distribution and speckle noise coefficients are modeled as Gaussian distribution. Method of Log Cumulants (MoLC) and Method of Moments (MoM) are used for parameter estimation of Nakagami distribution and noise free shearlet coefficients respectively. Then noise free shearlet coefficients are obtained using Maximum a Posteriori (MaP) estimation of noisy coefficients. The experimental results were presented by performing various experiments on synthetic and real US images. Subjective and objective quality assessment of the proposed method is presented and is compared with six other existing methods. The effectiveness of the proposed method over other methods can be seen from the obtained results

Despeckling Of Synthetic Aperture Radar Images Using Shearlet Transform

Synthetic Aperture Radar (SAR) is widely used for producing high quality imaging of Earth sur- face due to its capability of image acquisition in all- weather conditions. However, one limitation of SAR image is that image textures and fine details are usually contaminated with multiplicative granular noise named as speckle noise. This paper presents a speckle reduc- tion technique for SAR images based on statistical mod- elling of detail band shearlet coefficients (SC) in ho- momorphic environment. Modelling of SC correspond- ing to noiseless SAR image are carried out as Nor- mal Inverse Gaussian (NIG) distribution while speckle noise SC are modelled as Gaussian distribution. These SC are segmented as heterogeneous, strongly hetero- geneous and homogeneous regions depending upon the local statistics of images. Then maximum a posteri- ori (MAP) estimation is employed over SC that belong to homogenous and heterogenous region category. The performance of proposed method is compared with seven other methods based on objective and subjective quality measures. PSNR and SSIM metrics are used for objec- tive assessment of synthetic images and ENL metric is used for real SAR images. Subjective assessment is carried out by visualizing denoised images obtained from various methods. The comparative result analy- sis shows that for the proposed method, higher values of PSNR i.e. 26.08 dB, 25.39 dB and 23.82 dB and SSIM i.e. 0.81, 0.69 and 0.61 are obtained for Barbara im- age at noise variances 0.04, 0.1 and 0.15, respectively as compared to other methods. For other images also results obtained for proposed method are at higher side. Also, ENL for real SAR images show highest average value of 125.91 79.05. Hence, the proposed method sig- nifies its potential in comparison to other seven existing image denoising methods in terms of speckle denoising and edge preservation

A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three decades, several methods have been proposed for the reduction of speckle, or despeckling, in SAR images. Goal of this paper is making a comprehensive review of despeckling methods since their birth, over thirty years ago, highlighting trends and changing approaches over years. The concept of fully developed speckle is explained. Drawbacks of homomorphic filtering are pointed out. Assets of multiresolution despeckling, as opposite to spatial-domain despeckling, are highlighted. Also advantages of undecimated, or stationary, wavelet transforms over decimated ones are discussed. Bayesian estimators and probability density function (pdf) models in both spatial and multiresolution domains are reviewed. Scale-space varying pdf models, as opposite to scale varying models, are promoted. Promising methods following non-Bayesian approaches, like nonlocal (NL) filtering and total variation (TV) regularization, are reviewed and compared to spatial- and wavelet-domain Bayesian filters. Both established and new trends for assessment of despeckling are presented. A few experiments on simulated data and real COSMO-SkyMed SAR images highlight, on one side the costperformance tradeoff of the different methods, on the other side the effectiveness of solutions purposely designed for SAR heterogeneity and not fully developed speckle. Eventually, upcoming methods based on new concepts of signal processing, like compressive sensing, are foreseen as a new generation of despeckling, after spatial-domain and multiresolution-domain method

Adaptive Feature Engineering Modeling for Ultrasound Image Classification for Decision Support

Ultrasonography is considered a relatively safe option for the diagnosis of benign and malignant cancer lesions due to the low-energy sound waves used. However, the visual interpretation of the ultrasound images is time-consuming and usually has high false alerts due to speckle noise. Improved methods of collection image-based data have been proposed to reduce noise in the images; however, this has proved not to solve the problem due to the complex nature of images and the exponential growth of biomedical datasets. Secondly, the target class in real-world biomedical datasets, that is the focus of interest of a biopsy, is usually significantly underrepresented compared to the non-target class. This makes it difficult to train standard classification models like Support Vector Machine (SVM), Decision Trees, and Nearest Neighbor techniques on biomedical datasets because they assume an equal class distribution or an equal misclassification cost. Resampling techniques by either oversampling the minority class or under-sampling the majority class have been proposed to mitigate the class imbalance problem but with minimal success. We propose a method of resolving the class imbalance problem with the design of a novel data-adaptive feature engineering model for extracting, selecting, and transforming textural features into a feature space that is inherently relevant to the application domain. We hypothesize that by maximizing the variance and preserving as much variability in well-engineered features prior to applying a classifier model will boost the differentiation of the thyroid nodules (benign or malignant) through effective model building. Our proposed a hybrid approach of applying Regression and Rule-Based techniques to build our Feature Engineering and a Bayesian Classifier respectively. In the Feature Engineering model, we transformed images pixel intensity values into a high dimensional structured dataset and fitting a regression analysis model to estimate relevant kernel parameters to be applied to the proposed filter method. We adopted an Elastic Net Regularization path to control the maximum log-likelihood estimation of the Regression model. Finally, we applied a Bayesian network inference to estimate a subset for the textural features with a significant conditional dependency in the classification of the thyroid lesion. This is performed to establish the conditional influence on the textural feature to the random factors generated through our feature engineering model and to evaluate the success criterion of our approach. The proposed approach was tested and evaluated on a public dataset obtained from thyroid cancer ultrasound diagnostic data. The analyses of the results showed that the classification performance had a significant improvement overall for accuracy and area under the curve when then proposed feature engineering model was applied to the data. We show that a high performance of 96.00% accuracy with a sensitivity and specificity of 99.64%) and 90.23% respectively was achieved for a filter size of 13 × 13

Contrôle en temps réel de la précision du suivi indirect de tumeurs mobiles en radiothérapie

Le but de la radiothérapie est d’irradier les cellules cancéreuses tout en préservant au maximum les tissus sains environnants. Or, dans le cas du cancer du poumon, la respiration du patient engendre des mouvements de la tumeur pendant le traitement. Une solution possible est de repositionner continuellement le faisceau d’irradiation sur la cible tumorale en mouvement. L’e cacité et la sûreté de cette approche reposent sur la localisation précise en temps réel de la tumeur. Le suivi indirect consiste à inférer la position de la cible tumorale à partir de l’observation d’un signal substitut, visible en continu sans nécessiter de rayonnement ionisant. Un modèle de corrélation spatial doit donc être établi. Par ailleurs, pour compenser la latence du système, l’algorithme de suivi doit pouvoir également anticiper la position future de la cible. Parce que la respiration du patient varie dans le temps, les modèles de prédiction et de corrélation peuvent devenir imprécis. La prédiction de la position de la tumeur devrait alors idéalement être complétée par l’estimation des incertitudes associées aux prédictions. Dans la pratique clinique actuelle, ces incertitudes de positionnement en temps réel ne sont pas explicitement prédites. Cette thèse de doctorat s’intéresse au contrôle en temps réel de la précision du suivi indirect de tumeurs mobiles en radiothérapie. Dans un premier temps, une méthode bayésienne pour le suivi indirect en radiothérapie est développée. Cette approche, basée sur le filtre de Kalman, permet de prédire non seulement la position future de la tumeur à partir d’un signal substitut, mais aussi les incertitudes associées. Ce travail o re une première preuve de concept, et montre également le potentiel du foie comme substitut interne, qui apparait plus robuste et fiable que les marqueurs externes communément utilisés dans la pratique clinique. Dans un deuxième temps, une adaptation de la méthode est proposée afin d’améliorer sa robustesse face aux changements de respiration. Cette innovation permet de prédire des régions de confiance adaptatives, capables de détecter les erreurs de prédiction élevées, en se basant exclusivement sur l’observation du signal substitut. Les résultats révèlent qu’à sensibilité élevée (90%), une spécificité d’environ 50% est obtenue. Un processus de validation innovant basé sur ces régions de confiance adaptatives est ensuite évalué et comparé au processus conventionnel qui consiste en des mesures de la cible à intervalles de temps fixes et prédéterminés. Une version adaptative de la méthode bayésienne est donc développée afin d’intégrer des mesures occasionnelles de la position de la cible. Les résultats confirment que les incertitudes prédites par la méthode bayésienne permettent de détecter les erreurs de prédictions élevées, et démontrent que le processus de validation basé sur ces incertitudes a le potentiel d’être plus e cace que les validations régulières. Ces approches bayésiennes sont validées sur des séquences respiratoires de volontaires, acquises par imagerie par résonance magnétique (IRM) dynamique et interpolées à haute fréquence. Afin de compléter l’évaluation de la méthode bayésienne pour le suivi indirect, une validation expérimentale préliminaire est conduite sur des données cliniques de patients atteints de cancer du poumon. Les travaux de ce projet doctoral promettent une amélioration du contrôle en temps réel de la précision des prédictions lors des traitements de radiothérapie. Finalement, puisque l’imagerie ultrasonore pourrait être employée pour visualiser les substituts internes, une étude préliminaire sur l’évaluation automatique de la qualité des images ultrasonores est présentée. Ces résultats pourront être utilisés ultérieurement pour le suivi indirect en radiothérapie en vue d’optimiser les acquisitions ultrasonores pendant les traitements et faciliter l’extraction automatique du mouvement du substitut.The goal of radiotherapy is to irradiate cancer cells while maintaining a low dose of radiation to the surrounding healthy tissue. In the case of lung cancer, the patient’s breathing causes the tumor to move during treatment. One possible solution is to continuously reposition the irradiation beam on the moving target. The e ectiveness and safety of this approach rely on accurate real-time localization of the tumor. Indirect strategies derive the target positions from a correlation model with a surrogate signal, which is continuously monitored without the need for radiation-based imaging. In addition, to compensate for system latency, the tracking algorithm must also be able to anticipate the future position of the target. Because the patient’s breathing varies over time, prediction and correlation models can become inaccurate. Ideally, the prediction of the tumor location would also include an estimation of the uncertainty associated with the prediction. However, in current clinical practice, these real-time positioning uncertainties are not explicitly predicted. This doctoral thesis focuses on real-time control of the accuracy of indirect tracking of mobile tumors in radiotherapy. First, a Bayesian method is developed. This approach, based on Kalman filter theory, allows predicting both future target motion in real-time from a surrogate signal and associated uncertainty. This work o ers a first proof of concept, and also shows the potential of the liver as an internal substitute as it appears more robust and reliable than the external markers commonly used in clinical practice. Second, an adaptation of the method is proposed to improve its robustness against changes in breathing. This innovation enables the prediction of adaptive confidence regions that can be used to detect significant prediction errors, based exclusively on the observation of the surrogate signal. The results show that at high sensitivity (90%), a specificity of about 50% is obtained. A new validation process based on these adaptive confidence regions is then evaluated and compared to the conventional validation process (i.e., target measurements at fixed and predetermined time intervals). An adaptive version of the Bayesian method is therefore developed to valuably incorporate occasional measurements of the target position. The results confirm that the uncertainties predicted by the Bayesian method can detect high prediction errors, and demonstrate that the validation process based on these uncertainties has the potential to be more e cient and e ective than regular validations. For these studies, the proposed Bayesian methods are validated on respiratory sequences of volunteers, acquired by dynamic MRI and interpolated at high frequency. In order to complete the evaluation of the Bayesian method for indirect tracking, experimental validation is conducted on clinical data of patients with lung cancer. The work of this doctoral project promises to improve the real-time control of the accuracy of predictions during radiotherapy treatments. Finally, since ultrasound imaging could be used to visualize internal surrogates, a preliminary study on automatic ultrasound image quality assessment is presented. These results can later be used for indirect tracking in radiotherapy to optimize ultrasound acquisitions during treatments and facilitate the automatic estimation of surrogate motion