6 research outputs found
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 two-stage framework for optical coherence tomography angiography image quality improvement
IntroductionOptical Coherence Tomography Angiography (OCTA) is a new non-invasive imaging modality that gains increasing popularity for the observation of the microvasculatures in the retina and the conjunctiva, assisting clinical diagnosis and treatment planning. However, poor imaging quality, such as stripe artifacts and low contrast, is common in the acquired OCTA and in particular Anterior Segment OCTA (AS-OCTA) due to eye microtremor and poor illumination conditions. These issues lead to incomplete vasculature maps that in turn makes it hard to make accurate interpretation and subsequent diagnosis.MethodsIn this work, we propose a two-stage framework that comprises a de-striping stage and a re-enhancing stage, with aims to remove stripe noise and to enhance blood vessel structure from the background. We introduce a new de-striping objective function in a Stripe Removal Net (SR-Net) to suppress the stripe noise in the original image. The vasculatures in acquired AS-OCTA images usually exhibit poor contrast, so we use a Perceptual Structure Generative Adversarial Network (PS-GAN) to enhance the de-striped AS-OCTA image in the re-enhancing stage, which combined cyclic perceptual loss with structure loss to achieve further image quality improvement.Results and discussionTo evaluate the effectiveness of the proposed method, we apply the proposed framework to two synthetic OCTA datasets and a real AS-OCTA dataset. Our results show that the proposed framework yields a promising enhancement performance, which enables both conventional and deep learning-based vessel segmentation methods to produce improved results after enhancement of both retina and AS-OCTA modalities
Deep Learning Methods for Synthetic Aperture Radar Image Despeckling: An Overview of Trends and Perspectives
Synthetic aperture radar (SAR) images are affected by a spatially correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation. Despeckling is an important task that aims to remove such noise so as to improve the accuracy of all downstream image processing tasks. The first despeckling methods date back to the 1970s, and several model-based algorithms have been developed in the years since. The field has received growing attention, sparked by the availability of powerful deep learning models that have yielded excellent performance for inverse problems in image processing. This article surveys the literature on deep learning methods applied to SAR despeckling, covering both supervised and the more recent self-supervised approaches. We provide a critical analysis of existing methods, with the objective of recognizing the most promising research lines; identify the factors that have limited the success of deep models; and propose ways forward in an attempt to fully exploit the potential of deep learning for SAR despeckling
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Deep learning assisted MRI guided attenuation correction in PET
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPositron emission tomography (PET) is a unique imaging modality that provides physiological
and functional details of the tissue at the molecular level. However, the acquired PET images
have some limitations such as the attenuation. PET attenuation correction is an essential step to
obtain the full potential of PET quantification. With the wide use of hybrid PET/MR scanners,
magnetic resonance (MR) images are used to address the problem of PET attenuation correction.
The MR images segmentation is one simple and robust approach to create pseudo computed
tomography (CT) images, which are used to generate attenuation coefficient maps to correct the
PET attenuation. Recently, deep learning has been proposed and used as a promising technique
to efficiently perform MR and various medical images segmentation.
In this research work, deep learning guided segmentation approaches have been proposed
to enhance the bone class segmentation of MR brain images in order to generate accurate
pseudo-CT images. The first approach has introduced the combination of handcrafted features
with deep learning features to enrich the set of features. Multiresolution analysis techniques,
which generate multiscale and multidirectional coefficients of an image such as contourlet and
shearlet transforms, are applied and combined with deep convolutional neural network (CNN)
features. Different experiments have been conducted to investigate the number of selected
coefficients and the insertion location of the handcrafted features.
The second approach aims at reducing the segmentation algorithm’s complexity while
maintaining the segmentation performance. An attention based convolutional encode-decoder
network has been proposed to adaptively recalibrate the deep network features. This attention based
network consists of two different squeeze and excitation blocks that excite the features
spatially and channel wise. The two blocks are combined sequentially to decrease the number
of network’s parameters and reduces the model complexity. The third approach has been focuses on the application of transfer learning from different MR sequences such as T1 weighted (T1-w) and T2 weighted (T2-w) images. A
pretrained model with T1-w MR sequences is fine tuned to perform the segmentation of T2-w
images. Multiple fine tuning approaches and experiments have been conducted to study the best
fine tuning mechanism that is able to build an efficient segmentation model for both T1-w and
T2-w segmentation. Clinical datasets of fifty patients with different conditions and diagnosis have been
used to carry an objective evaluation to measure the segmentation performance of the results
obtained by the three proposed methods. The first and second approaches have been validated
with other studies in the literature that applied deep network based segmentation technique to
perform MR based attenuation correction for PET images. The proposed methods have shown
an enhancement in the bone segmentation with an increase of dice similarity coefficient (DSC)
from 0.6179 to 0.6567 using an ensemble of CNNs with an improvement percentage of 6.3%.
The proposed excitation-based CNN has decreased the model complexity by decreasing the
number of trainable parameters by more than 46% where less computing resources are required
to train the model. The proposed hybrid transfer learning method has shown its superiority to
build a multi-sequences (T1-w and T2-w) segmentation approach compared to other applied
transfer learning methods especially with the bone class where the DSC is increased from 0.3841
to 0.5393. Moreover, the hybrid transfer learning approach requires less computing time than
transfer learning using open and conservative fine tuning
Real-time Ultrasound Signals Processing: Denoising and Super-resolution
Ultrasound acquisition is widespread in the biomedical field, due to its properties of low cost, portability, and non-invasiveness for the patient. The processing and analysis of US signals, such as images, 2D videos, and volumetric images, allows the physician to monitor the evolution of the patient's disease, and support diagnosis, and treatments (e.g., surgery). US images are affected by speckle noise, generated by the overlap of US waves. Furthermore, low-resolution images are acquired when a high acquisition frequency is applied to accurately characterise the behaviour of anatomical features that quickly change over time. Denoising and super-resolution of US signals are relevant to improve the visual evaluation of the physician and the performance and accuracy of processing methods, such as segmentation and classification. The main requirements for the processing and analysis of US signals are real-time execution, preservation of anatomical features, and reduction of artefacts. In this context, we present a novel framework for the real-time denoising of US 2D images based on deep learning and high-performance computing, which reduces noise while preserving anatomical features in real-time execution. We extend our framework to the denoise of arbitrary US signals, such as 2D videos and 3D images, and we apply denoising algorithms that account for spatio-temporal signal properties into an image-to-image deep learning model. As a building block of this framework, we propose a novel denoising method belonging to the class of low-rank approximations, which learns and predicts the optimal thresholds of the Singular Value Decomposition. While previous denoise work compromises the computational cost and effectiveness of the method, the proposed framework achieves the results of the best denoising algorithms in terms of noise removal, anatomical feature preservation, and geometric and texture properties conservation, in a real-time execution that respects industrial constraints. The framework reduces the artefacts (e.g., blurring) and preserves the spatio-temporal consistency among frames/slices; also, it is general to the denoising algorithm, anatomical district, and noise intensity. Then, we introduce a novel framework for the real-time reconstruction of the non-acquired scan lines through an interpolating method; a deep learning model improves the results of the interpolation to match the target image (i.e., the high-resolution image). We improve the accuracy of the prediction of the reconstructed lines through the design of the network architecture and the loss function. %The design of the deep learning architecture and the loss function allow the network to improve the accuracy of the prediction of the reconstructed lines. In the context of signal approximation, we introduce our kernel-based sampling method for the reconstruction of 2D and 3D signals defined on regular and irregular grids, with an application to US 2D and 3D images. Our method improves previous work in terms of sampling quality, approximation accuracy, and geometry reconstruction with a slightly higher computational cost. For both denoising and super-resolution, we evaluate the compliance with the real-time requirement of US applications in the medical domain and provide a quantitative evaluation of denoising and super-resolution methods on US and synthetic images. Finally, we discuss the role of denoising and super-resolution as pre-processing steps for segmentation and predictive analysis of breast pathologies