Reliable and Efficient coding Technique for Compression of Medical Images based on Region of Interest using Directional Filter Banks

Medical images carry huge and vital information. It is necessary to compress the medical images without losing its vital-ness. The proposed algorithm presents a new coding technique based on  image compression using contourlet transform used in different modalities of medical imaging. Recent reports on natural image compression have shown superior performance of contourlet transform, a new extension to the wavelet transform in two dimensions using nonseparable and directional filter banks. As far as medical images are concerned the diagnosis part (ROI) is of much important compared to other regions. Therefore those portions are segmented from the whole image using  fuzzy C-means(FCM) clustering technique. Contourlet transform is then applied to ROI portion which performs Laplacian Pyramid(LP) and Directional Filter Banks. The region of less significance are compressed using Discrete Wavelet Transform and finally modified embedded zerotree wavelet algorithm is applied which uses six symbols instead of four symbols used in Shapiro’s EZW to the resultant image which shows better PSNR and high compression ratio.Â

A Novel Multiscale Edge Detection Approach Based on Nonsubsampled Contourlet Transform and Edge Tracking

Edge detection is a fundamental task in many computer vision applications. In this paper, we propose a novel multiscale edge detection approach based on the nonsubsampled contourlet transform (NSCT): a fully shift-invariant, multiscale, and multidirection transform. Indeed, unlike traditional wavelets, contourlets have the ability to fully capture directional and other geometrical features for images with edges. Firstly, compute the NSCT of the input image. Secondly, the K-means clustering algorithm is applied to each level of the NSCT for distinguishing noises from edges. Thirdly, we select the edge point candidates of the input image by identifying the NSCT modulus maximum at each scale. Finally, the edge tracking algorithm from coarser to finer is proposed to improve robustness against spurious responses and accuracy in the location of the edges. Experimental results show that the proposed method achieves better edge detection performance compared with the typical methods. Furthermore, the proposed method also works well for noisy images

Automated Segmentation of Cerebral Aneurysm Using a Novel Statistical Multiresolution Approach

Cerebral Aneurysm (CA) is a vascular disease that threatens the lives of many adults. It a ects almost 1:5 - 5% of the general population. Sub- Arachnoid Hemorrhage (SAH), resulted by a ruptured CA, has high rates of morbidity and mortality. Therefore, radiologists aim to detect it and diagnose it at an early stage, by analyzing the medical images, to prevent or reduce its damages. The analysis process is traditionally done manually. However, with the emerging of the technology, Computer-Aided Diagnosis (CAD) algorithms are adopted in the clinics to overcome the traditional process disadvantages, as the dependency of the radiologist's experience, the inter and intra observation variability, the increase in the probability of error which increases consequently with the growing number of medical images to be analyzed, and the artifacts added by the medical images' acquisition methods (i.e., MRA, CTA, PET, RA, etc.) which impedes the radiologist' s work. Due to the aforementioned reasons, many research works propose di erent segmentation approaches to automate the analysis process of detecting a CA using complementary segmentation techniques; but due to the challenging task of developing a robust reproducible reliable algorithm to detect CA regardless of its shape, size, and location from a variety of the acquisition methods, a diversity of proposed and developed approaches exist which still su er from some limitations. This thesis aims to contribute in this research area by adopting two promising techniques based on the multiresolution and statistical approaches in the Two-Dimensional (2D) domain. The rst technique is the Contourlet Transform (CT), which empowers the segmentation by extracting features not apparent in the normal image scale. While the second technique is the Hidden Markov Random Field model with Expectation Maximization (HMRF-EM), which segments the image based on the relationship of the neighboring pixels in the contourlet domain. The developed algorithm reveals promising results on the four tested Three- Dimensional Rotational Angiography (3D RA) datasets, where an objective and a subjective evaluation are carried out. For the objective evaluation, six performance metrics are adopted which are: accuracy, Dice Similarity Index (DSI), False Positive Ratio (FPR), False Negative Ratio (FNR), speci city, and sensitivity. As for the subjective evaluation, one expert and four observers with some medical background are involved to assess the segmentation visually. Both evaluations compare the segmented volumes against the ground truth data

Directional edge and texture representations for image processing

An efficient representation for natural images is of fundamental importance in image processing and analysis. The commonly used separable transforms such as wavelets axe not best suited for images due to their inability to exploit directional regularities such as edges and oriented textural patterns; while most of the recently proposed directional schemes cannot represent these two types of features in a unified transform. This thesis focuses on the development of directional representations for images which can capture both edges and textures in a multiresolution manner. The thesis first considers the problem of extracting linear features with the multiresolution Fourier transform (MFT). Based on a previous MFT-based linear feature model, the work extends the extraction method into the situation when the image is corrupted by noise. The problem is tackled by the combination of a "Signal+Noise" frequency model, a refinement stage and a robust classification scheme. As a result, the MFT is able to perform linear feature analysis on noisy images on which previous methods failed. A new set of transforms called the multiscale polar cosine transforms (MPCT) are also proposed in order to represent textures. The MPCT can be regarded as real-valued MFT with similar basis functions of oriented sinusoids. It is shown that the transform can represent textural patches more efficiently than the conventional Fourier basis. With a directional best cosine basis, the MPCT packet (MPCPT) is shown to be an efficient representation for edges and textures, despite its high computational burden. The problem of representing edges and textures in a fixed transform with less complexity is then considered. This is achieved by applying a Gaussian frequency filter, which matches the disperson of the magnitude spectrum, on the local MFT coefficients. This is particularly effective in denoising natural images, due to its ability to preserve both types of feature. Further improvements can be made by employing the information given by the linear feature extraction process in the filter's configuration. The denoising results compare favourably against other state-of-the-art directional representations

Breast Mass Classification Based on Hybrid Discrete Cosine Transformation–Haar Wavelet Transformation

Mammography is the most effective procedure for the early detection of breast cancer. In this paper an efficient a Computer Aided Diagnosis (CADx) system is proposed to discriminate between benign and malignant. The system comprises mainly of three steps: preprocessing of the images, feature extraction, and finally classification and performance analysis. The case sample mammographic images, originating from the mini MIAS (Mammographic Image Analysis Society) database. In the preprocessing phase the ROI is cropped and resized by 128 x 128. at the very beginning of the feature extraction process, we have applied Haar Wavelet Transform (HWT) for five levels and, in each level, Discrete Cosine Transform applied with various selection of coefficients. After that, different types of features are fed into the feature similarity measure City Block for the diagnosis of breast cancer. The images are of two classes benign and malignant classes. Finally, K-Nearest Number is employed here as a classifier. In our proposed system, we found competitive results

A Novel Multimodal Image Fusion Method Using Hybrid Wavelet-based Contourlet Transform

Various image fusion techniques have been studied to meet the requirements of different applications such as concealed weapon detection, remote sensing, urban mapping, surveillance and medical imaging. Combining two or more images of the same scene or object produces a better application-wise visible image. The conventional wavelet transform (WT) has been widely used in the field of image fusion due to its advantages, including multi-scale framework and capability of isolating discontinuities at object edges. However, the contourlet transform (CT) has been recently adopted and applied to the image fusion process to overcome the drawbacks of WT with its own advantages. Based on the experimental studies in this dissertation, it is proven that the contourlet transform is more suitable than the conventional wavelet transform in performing the image fusion. However, it is important to know that the contourlet transform also has major drawbacks. First, the contourlet transform framework does not provide shift-invariance and structural information of the source images that are necessary to enhance the fusion performance. Second, unwanted artifacts are produced during the image decomposition process via contourlet transform framework, which are caused by setting some transform coefficients to zero for nonlinear approximation. In this dissertation, a novel fusion method using hybrid wavelet-based contourlet transform (HWCT) is proposed to overcome the drawbacks of both conventional wavelet and contourlet transforms, and enhance the fusion performance. In the proposed method, Daubechies Complex Wavelet Transform (DCxWT) is employed to provide both shift-invariance and structural information, and Hybrid Directional Filter Bank (HDFB) is used to achieve less artifacts and more directional information. DCxWT provides shift-invariance which is desired during the fusion process to avoid mis-registration problem. Without the shift-invariance, source images are mis-registered and non-aligned to each other; therefore, the fusion results are significantly degraded. DCxWT also provides structural information through its imaginary part of wavelet coefficients; hence, it is possible to preserve more relevant information during the fusion process and this gives better representation of the fused image. Moreover, HDFB is applied to the fusion framework where the source images are decomposed to provide abundant directional information, less complexity, and reduced artifacts. The proposed method is applied to five different categories of the multimodal image fusion, and experimental study is conducted to evaluate the performance of the proposed method in each multimodal fusion category using suitable quality metrics. Various datasets, fusion algorithms, pre-processing techniques and quality metrics are used for each fusion category. From every experimental study and analysis in each fusion category, the proposed method produced better fusion results than the conventional wavelet and contourlet transforms; therefore, its usefulness as a fusion method has been validated and its high performance has been verified