Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation

Copyright @ 2011 Shadi AlZubi et al. This article has been made available through the Brunel Open Access Publishing Fund.The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise

3D medical volume segmentation using hybrid multiresolution statistical approaches

This article is available through the Brunel Open Access Publishing Fund. Copyright © 2010 S AlZu’bi and A Amira.3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physical entities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enables the preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets have been deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medical volume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extraction which can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carried out to evaluate 2D and 3D techniques which reveals that 3D methodologies can accurately detect the Region Of Interest (ROI). Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations

A Free-Form Prediction for the Reappearance of Supernova Refsdal in the Hubble Frontier Fields Cluster MACSJ1149.5+2223

The massive cluster MACSJ1149.5+2223(z=0.544) displays five very large lensed images of a well resolved spiral galaxy at $z_{\rm spect}=1.491$. It is within one of these images that the first example of a multiply-lensed supernova has been detected recently as part of the Grism Lens-Amplified Survey from Space. The depth of this data also reveals many HII regions within the lensed spiral galaxy which we identify between the five counter-images. Here we expand the capability of our free-form method to incorporate these HII regions locally, with other reliable lensed galaxies added for a global solution. This improved accuracy allows us to estimate when the Refsdal supernova will appear within the other lensed images of the spiral galaxy to an accuracy of $\sim$ 7\%. We predict this supernova will reappear in one of the counter-images (RA=11:49:36.025, DEC=+22:23:48.11, J2000) and on November 1$^{st}$ 2015 (with an estimated error of $\pm$ 25 days) it will be at the same phase as it was when it was originally discovered, offering a unique opportunity to study the early phases of this supernova and to examine the consistency of the mass model and the cosmological model that have an impact on the time delay prediction.Comment: 12 images, 11 pages. Mateches accepted version in MNRAS. MNRAS in pres

A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity

The richness of natural images makes the quest for optimal representations in image processing and computer vision challenging. The latter observation has not prevented the design of image representations, which trade off between efficiency and complexity, while achieving accurate rendering of smooth regions as well as reproducing faithful contours and textures. The most recent ones, proposed in the past decade, share an hybrid heritage highlighting the multiscale and oriented nature of edges and patterns in images. This paper presents a panorama of the aforementioned literature on decompositions in multiscale, multi-orientation bases or dictionaries. They typically exhibit redundancy to improve sparsity in the transformed domain and sometimes its invariance with respect to simple geometric deformations (translation, rotation). Oriented multiscale dictionaries extend traditional wavelet processing and may offer rotation invariance. Highly redundant dictionaries require specific algorithms to simplify the search for an efficient (sparse) representation. We also discuss the extension of multiscale geometric decompositions to non-Euclidean domains such as the sphere or arbitrary meshed surfaces. The etymology of panorama suggests an overview, based on a choice of partially overlapping "pictures". We hope that this paper will contribute to the appreciation and apprehension of a stream of current research directions in image understanding.Comment: 65 pages, 33 figures, 303 reference