1,067 research outputs found
Kajian motivasi ekstrinsik di antara Pelajar Lepasan Sijil dan Diploma Politeknik Jabatan Kejuruteraan Awam KUiTTHO
Kajian ini dijalankan untuk menyelidiki pengaruh dorongan keluarga, cara pengajaran pensyarah, pengaruh rakan sebaya dan kemudahan infrastruktur terhadap motivasi ekstrinsik bagi pelajar tahun tiga dan tahun empat lepasan sijil dan diploma politeknik Jabatan Kejuruteraan Awain Kolej Universiti Teknologi Tun Hussein Onn. Sampel kajian ini beijumlah 87 orang bagi pelajar lepasan sijil politeknik dan 38 orang bagi lepasan diploma politeknik. Data kajian telah diperolehi melalui borang soal selidik dan telah dianalisis menggunakan perisian SPSS (Statical Package For Sciences). Hasil kajian telah dipersembahkan dalam bentuk jadual dan histohgrapi. Analisis kajian mendapati bahawa kedua-dua kumpulan setuju bahawa faktor-faktor di atas memberi kesan kepada motivasi ekstrinsik mereka. Dengan kata lain faktpr-faktor tersebut penting dalam membentuk pelajar mencapai kecemerlangan akademik
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
Techniques for enhancing digital images
The images obtain from either research studies or optical instruments are
often corrupted with noise. Image denoising involves the manipulation of image
data to produce a visually high quality image. This thesis reviews the existing
denoising algorithms and the filtering approaches available for enhancing images
and/or data transmission.
Spatial-domain and Transform-domain digital image filtering algorithms
have been used in the past to suppress different noise models. The different noise
models can be either additive or multiplicative. Selection of the denoising algorithm
is application dependent. It is necessary to have knowledge about the noise present
in the image so as to select the appropriated denoising algorithm. Noise models
may include Gaussian noise, Salt and Pepper noise, Speckle noise and Brownian
noise. The Wavelet Transform is similar to the Fourier transform with a completely
different merit function. The main difference between Wavelet transform and
Fourier transform is that, in the Wavelet Transform, Wavelets are localized in both
time and frequency. In the standard Fourier Transform, Wavelets are only localized
in frequency. Wavelet analysis consists of breaking up the signal into shifted and
scales versions of the original (or mother) Wavelet. The Wiener Filter (mean
squared estimation error) finds implementations as a LMS filter (least mean
squares), RLS filter (recursive least squares), or Kalman filter.
Quantitative measure (metrics) of the comparison of the denoising algorithms
is provided by calculating the Peak Signal to Noise Ratio (PSNR), the Mean Square
Error (MSE) value and the Mean Absolute Error (MAE) evaluation factors. A
combination of metrics including the PSNR, MSE, and MAE are often required to
clearly assess the model performance
SVM Classification of MRI Brain Images for Computer-Assisted Diagnosis
Magnetic Resonance Imaging is a powerful technique that helps in the diagnosis of various medical conditions. MRI Image pre-processing followed by detection of brain abnormalities, such as brain tumors, are considered in this work. These images are often corrupted by noise from various sources. The Discrete Wavelet Transforms (DWT) with details thresholding is used for efficient noise removal followed by edge detection and threshold segmentation of the denoised images. Segmented image features are then extracted using morphological operations. These features are finally used to train an improved Support Vector Machine classifier that uses a Gausssian radial basis function kernel. The performance of the classifier is evaluated and the results of the classification show that the proposed scheme accurately distinguishes normal brain images from the abnormal ones and benign lesions from malignant tumours. The accuracy of the classification is shown to be 100% which is superior to the results reported in the literature
Pattern identification of biomedical images with time series: contrasting THz pulse imaging with DCE-MRIs
Objective
We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities.
Methods
Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed.
Validation
Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed.
Results
Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis.
Conclusion
The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community
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
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