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Noise suppression in MR (Magnetic Resonance) images is a critical task; conventional signal
processing techniques are not always suitable as spatial resolution may lose during noise
suppression process. Therefore noise suppression ought to be performed in a manner so as to
preserve the actual pattern of the image. Non-homogeneous noise is one of the challenges faced
in image processing. This thesis work; specifically focuses on non-homogeneous noise
suppression method for MR images.
Wavelet Analysis has widely been used for image processing including image de-noising, edge
detection and segmentation. The existing wavelet de-noising methods are focused on
homogeneous noise removal, using same threshold for entire image. If the image contains
different burst of random noise, these conventional methods are not sufficient for effective
noise removal. The quality of the post-processed image is further affected if these noise
patterns cover hard to find malignant areas, which possibly increases the false alarm for
diagnostic imaging. In order to improve the early detection of possible malignant areas, the
quality of the post-processed image requires effective de-noising techniques, which can be
adapted with the nature of noise burst. The fuzzy rule based wavelet thresholding method has
been explored in this research for effective noise removal from an image with an array of
complexities. In order to develop a robust system closer to real image with non-homogeneous
noise, a complex range of noise patterns have been incorporated in MR images.
The initial phase of the dissertation work involves the synthesis of non-homogeneous noise on
various MR images. Real MR images without noise burst were used as a benchmark. The de-
noised images are compared with their clean counterparts for measuring the effectiveness of the
technique. A novel image synthesis process has been developed for analyzing the image de-
noising and segmentation. Some of the images contain various sizes of malignant patterns for
full scale analysis of image de-noising and fuzzy image segmentation. The main focus of the
analysis is the brain image, as it requires rigorous image assessments for an effective
classification and detection of patterns.
The second phase of the dissertation work expounds the wavelet thresholding for various sets
of images. An in-depth investigation of fuzzy rule based optimizer for adapting the wavelet
threshold for effective noise suppression has been examined. In this technique, the threshold is
further optimized, based on number of criterion including; the intensity, location and size of the
noise burst over the malignant patterns. Therefore the present technique improves the post
processing diagnostic of images containing small pattern(s) hidden under noise bursts, which
otherwise goes undetected.
The third phase of the dissertation work studies the impact of non-homogeneous noise on the
performance of fuzzy image clustering algorithm. Various results were analyzed for clean,
noisy and de-noised images. The purpose here is to segment the malignant areas of noisy brain
MRI for effective tumor detection.
Fuzzy rule based optimizer plays an important role for adapting the wavelet threshold for the
region of interest. The fuzzy information of image contours and noise burst transformed into
crisp control decision signals for adapting the threshold. In addition, it was found that the noisy
image with no tumor has a false possibility of detecting benign pattern as malignant area.
Other research outcome includes the detection of patterns in an image with invisible noise
bursts using Multi-resolution Analysis. The result of this course of action is obtained in the
diagonal detail components of multi-level decomposition.
The difficulties observed in the prevailing methodology include the limited set of research
studies conducted to address the issue of non-homogeneous noise in MR Images and the
limited accessibility of real images. A good source of validation is the comparison of the de-
noised image with that of clean image.
Impact of non-homogeneous noise has been explored using directional wavelet. This analysis
demonstrates how adversely, different noise patterns affect the computational performance of
curvelets and ridglet.
The main outcomes of this technique include the impact of non-
homogeneous noise on wavelet and curvelet based de-noising methods. An important attribute
of this research, is improved methodology for malignant patterns detection in noisy MR
Images. This, in turn, makes possible the better development of image diagnostic tools. |
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