127 research outputs found
Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments
The localized nature of curvelet functions, together with their frequency and
dip characteristics, makes the curvelet transform an excellent choice for
processing seismic data. In this work, a denoising method is proposed based on
a combination of the curvelet transform and a whitening filter along with
procedure for noise variance estimation. The whitening filter is added to get
the best performance of the curvelet transform under coherent and incoherent
correlated noise cases, and furthermore, it simplifies the noise estimation
method and makes it easy to use the standard threshold methodology without
digging into the curvelet domain. The proposed method is tested on
pseudo-synthetic data by adding noise to real noise-less data set of the
Netherlands offshore F3 block and on the field data set from east Texas, USA,
containing ground roll noise. Our experimental results show that the proposed
algorithm can achieve the best results under all types of noises (incoherent or
uncorrelated or random, and coherent noise)
Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review
This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated
Parameter optimization for local polynomial approximation based intersection confidence interval filter using genetic algorithm: an application for brain MRI image de-noising
Magnetic resonance imaging (MRI) is extensively exploited for more accuratepathological changes as well as diagnosis. Conversely, MRI suffers from variousshortcomings such as ambient noise from the environment, acquisition noise from theequipment, the presence of background tissue, breathing motion, body fat, etc.Consequently, noise reduction is critical as diverse types of the generated noise limit the efficiency of the medical image diagnosis. Local polynomial approximation basedintersection confidence interval (LPA-ICI) filter is one of the effective de-noising filters.This filter requires an adjustment of the ICI parameters for efficient window size selection.From the wide range of ICI parametric values, finding out the best set of tunes values is itselfan optimization problem. The present study proposed a novel technique for parameteroptimization of LPA-ICI filter using genetic algorithm (GA) for brain MR imagesde-noising. The experimental results proved that the proposed method outperforms theLPA-ICI method for de-noising in terms of various performance metrics for different noisevariance levels. Obtained results reports that the ICI parameter values depend on the noisevariance and the concerned under test image
Efficient reconfigurable architectures for 3D medical image compression
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Recently, the more widespread use of three-dimensional (3-D) imaging modalities,
such as magnetic resonance imaging (MRI), computed tomography (CT), positron
emission tomography (PET), and ultrasound (US) have generated a massive amount
of volumetric data. These have provided an impetus to the development of other
applications, in particular telemedicine and teleradiology. In these fields, medical
image compression is important since both efficient storage and transmission of data
through high-bandwidth digital communication lines are of crucial importance.
Despite their advantages, most 3-D medical imaging algorithms are computationally intensive with matrix transformation as the most fundamental operation involved in the transform-based methods. Therefore, there is a real need for high-performance systems, whilst keeping architectures exible to allow
for quick upgradeability with real-time applications. Moreover, in order to obtain
efficient solutions for large medical volumes data, an efficient implementation of
these operations is of significant importance. Reconfigurable hardware, in the form of field programmable gate arrays (FPGAs) has been proposed as viable system
building block in the construction of high-performance systems at an economical price.
Consequently, FPGAs seem an ideal candidate to harness and exploit their inherent
advantages such as massive parallelism capabilities, multimillion gate counts, and
special low-power packages. The key achievements of the work presented in this thesis are summarised as follows. Two architectures for 3-D Haar wavelet transform (HWT) have been proposed based on transpose-based computation and partial reconfiguration suitable for 3-D medical imaging applications. These applications require continuous hardware servicing, and as a result dynamic partial reconfiguration (DPR) has been introduced. Comparative study for both non-partial and partial reconfiguration implementation has shown that DPR offers many advantages and leads to a compelling solution for implementing computationally intensive applications such as 3-D medical image compression. Using DPR, several large systems are mapped to small hardware resources, and the area, power consumption as well as maximum frequency are
optimised and improved. Moreover, an FPGA-based architecture of the finite Radon transform (FRAT)with three design strategies has been proposed: direct implementation of pseudo-code with a sequential or pipelined description, and block random access memory (BRAM)- based method. An analysis with various medical imaging modalities has been carried out. Results obtained for image de-noising implementation using FRAT exhibits
promising results in reducing Gaussian white noise in medical images. In terms of
hardware implementation, promising trade-offs on maximum frequency, throughput
and area are also achieved. Furthermore, a novel hardware implementation of 3-D medical image compression system with context-based adaptive variable length coding (CAVLC)
has been proposed. An evaluation of the 3-D integer transform (IT) and the discrete
wavelet transform (DWT) with lifting scheme (LS) for transform blocks reveal that
3-D IT demonstrates better computational complexity than the 3-D DWT, whilst
the 3-D DWT with LS exhibits a lossless compression that is significantly useful for
medical image compression. Additionally, an architecture of CAVLC that is capable
of compressing high-definition (HD) images in real-time without any buffer between
the quantiser and the entropy coder is proposed. Through a judicious parallelisation, promising results have been obtained with limited resources. In summary, this research is tackling the issues of massive 3-D medical volumes data that requires compression as well as hardware implementation to accelerate the
slowest operations in the system. Results obtained also reveal a significant achievement in terms of the architecture efficiency and applications performance.Ministry of Higher Education Malaysia (MOHE),
Universiti Tun Hussein Onn Malaysia (UTHM) and the British Counci
Image fusion using Wavelet Transform: A Review
An Image fusion is the development of amalgamating two or more image of common characteristic to form a single image which acquires all the essential features of original image Nowadays lots of work is going to be done on the field of image fusion and also used in various application such as medical imaging and multi spectra sensor image fusing etc For fusing the image various techniques has been proposed by different author such as wavelet transform IHS and PCA based methods etc In this paper literature of the image fusion with wavelet transform is discussed with its merits and demerit
Effective denoising and classification of hyperspectral images using curvelet transform and singular spectrum analysis
Hyperspectral imaging (HSI) classification has become a popular research topic in recent years, and effective feature extraction is an important step before the classification task. Traditionally, spectral feature extraction techniques are applied to the HSI data cube directly. This paper presents a novel algorithm for HSI feature extraction by exploiting the curvelet transformed domain via a relatively new spectral feature processing technique – singular spectrum analysis (SSA). Although the wavelet transform has been widely applied for HSI data analysis, the curvelet transform is employed in this paper since it is able to separate image geometric details and background noise effectively. Using the support vector machine (SVM) classifier, experimental results have shown that features extracted by SSA on curvelet coefficients have better performance in terms of classification accuracies over features extracted on wavelet coefficients. Since the proposed approach mainly relies on SSA for feature extraction on the spectral dimension, it actually belongs to the spectral feature extraction category. Therefore, the proposed method has also been compared with some state-of-the-art spectral feature extraction techniques to show its efficacy. In addition, it has been proven that the proposed method is able to remove the undesirable artefacts introduced during the data acquisition process as well. By adding an extra spatial post-processing step to the classified map achieved using the proposed approach, we have shown that the classification performance is comparable with several recent spectral-spatial classification methods
Joint Bilateral Filter for Signal Recovery from Phase Preserved Curvelet Coefficients for Image Denoising
Thresholding of Curvelet Coefficients, for image denoising, drains out subtle
signal component in noise subspace. This produces ringing artifacts near edges
and granular effect in the denoised image. We found the noise sensitivity of
Curvelet phases (in contrast to their magnitude) reduces with higher noise
level. Thus, we preserved the phase of the coefficients below threshold at
coarser scale and estimated their magnitude by Joint Bilateral Filtering (JBF)
technique from the thresholded and noisy coefficients. In the finest scale, we
apply Bilateral Filter (BF) to keep edge information. Further, the Guided Image
Filter (GIF) is applied on the reconstructed image to localize the edges and to
preserve the small image details and textures. The lower noise sensitivity of
Curvelet phase at higher noise strength accelerate the performance of proposed
method over several state-of-theart techniques and provides comparable outcome
at lower noise levels.Comment: 10 pages, 8 figures. 3 tables, journa
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