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Azhar Quddus

Bio: Azhar Quddus is an academic researcher from University of Waterloo. The author has contributed to research in topics: Wavelet transform & Wavelet. The author has an hindex of 13, co-authored 34 publications receiving 535 citations. Previous affiliations of Azhar Quddus include Sunnybrook Health Sciences Centre & Morpho.

Papers
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Journal ArticleDOI
TL;DR: Lesion Explorer was developed as the final component of a comprehensive volumetric segmentation and parcellation image processing stream built upon previously published methods and showed high inter-rater and inter-method reliability both globally and regionally.

116 citations

Journal ArticleDOI
TL;DR: A novel technique for wavelet-based corner detection using singular value decomposition (SVD) facilitates the selection of global natural scale in discrete wavelet transform.

64 citations

Journal ArticleDOI
TL;DR: In this article, an improved wavelet-based technique for corner detection in 2D planar curves is presented, which is simple to implement and computationally efficient and exploits wavelet transform modulus maxima to detect corners.
Abstract: An improved, wavelet-based technique for corner detection in 2D planar curves is presented. This boundary based technique is simple to implement and computationally efficient and exploits the wavelet transform modulus maxima to detect corners The proposed algorithm is robust with respect to object geometry. Results for noisy conditions are also reported.

43 citations

Journal ArticleDOI
01 May 2012
TL;DR: In this study, support vector machines (SVM) are used for identifying 3-D MR volume and for performing semantic classification of the human brain into various semantic regions and an image registration-based retrieval framework is developed.
Abstract: Practitioners in the area of neurology often need to retrieve multimodal magnetic resonance (MR) images of the brain to study disease progression and to correlate observations across multiple subjects In this paper, a novel technique for retrieving 2-D MR images (slices) in 3-D brain volumes is proposed Given a 2-D MR query slice, the technique identifies the 3-D volume among multiple subjects in the database, associates the query slice with a specific region of the brain, and retrieves the matching slice within this region in the identified volumes The proposed technique is capable of retrieving an image in multimodal and noisy scenarios In this study, support vector machines (SVM) are used for identifying 3-D MR volume and for performing semantic classification of the human brain into various semantic regions In order to achieve reliable image retrieval performance in the presence of misalignments, an image registration-based retrieval framework is developed The proposed retrieval technique is tested on various modalities The test results reveal superior robustness performance with respect to accuracy, speed, and multimodality

38 citations

Proceedings ArticleDOI
01 Jan 2005
TL;DR: The results indicate that the proposed approach can handle MR field inhomogeneities quite well and is completely independent from manual selection process so that it can be run under batch mode.
Abstract: The use of two powerful classification techniques (boosting and SVM) is explored for the segmentation of white-matter lesions in the MRI scans of human brain. Simple features are generated from proton density (PD) scans. Radial basis function (RBF) based Adaboost technique and support vector machines (SVM) are employed for this task. The classifiers are trained on severe, moderate and mild cases. The segmentation is performed in T1 acquisition space rather than standard space (with more slices). Hence, the proposed approach requires less time for manual verification. The results indicate that the proposed approach can handle MR field inhomogeneities quite well and is completely independent from manual selection process so that it can be run under batch mode. Segmentation performance comparison with manual detection is also provided

33 citations


Cited by
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Journal ArticleDOI
TL;DR: This Position Paper summarises the main outcomes of this international effort to provide the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE).
Abstract: Cerebral small vessel disease (SVD) is a common accompaniment of ageing. Features seen on neuroimaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. SVD can present as a stroke or cognitive decline, or can have few or no symptoms. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive deficits, physical disabilities, and other symptoms of neurodegeneration. Terminology and definitions for imaging the features of SVD vary widely, which is also true for protocols for image acquisition and image analysis. This lack of consistency hampers progress in identifying the contribution of SVD to the pathophysiology and clinical features of common neurodegenerative diseases. We are an international working group from the Centres of Excellence in Neurodegeneration. We completed a structured process to develop definitions and imaging standards for markers and consequences of SVD. We aimed to achieve the following: first, to provide a common advisory about terms and definitions for features visible on MRI; second, to suggest minimum standards for image acquisition and analysis; third, to agree on standards for scientific reporting of changes related to SVD on neuroimaging; and fourth, to review emerging imaging methods for detection and quantification of preclinical manifestations of SVD. Our findings and recommendations apply to research studies, and can be used in the clinical setting to standardise image interpretation, acquisition, and reporting. This Position Paper summarises the main outcomes of this international effort to provide the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE).

3,691 citations

Journal ArticleDOI
TL;DR: A new heuristic for feature detection is presented and, using machine learning, a feature detector is derived from this which can fully process live PAL video using less than 5 percent of the available processing time.
Abstract: The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is important because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations. The efficiency is important because this determines whether the detector combined with further processing can operate at frame rate. Three advances are described in this paper. First, we present a new heuristic for feature detection and, using machine learning, we derive a feature detector from this which can fully process live PAL video using less than 5 percent of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115 percent, SIFT 195 percent). Second, we generalize the detector, allowing it to be optimized for repeatability, with little loss of efficiency. Third, we carry out a rigorous comparison of corner detectors based on the above repeatability criterion applied to 3D scenes. We show that, despite being principally constructed for speed, on these stringent tests, our heuristic detector significantly outperforms existing feature detectors. Finally, the comparison demonstrates that using machine learning produces significant improvements in repeatability, yielding a detector that is both very fast and of very high quality.

1,847 citations

Journal ArticleDOI

1,008 citations

Journal ArticleDOI
TL;DR: The findings suggest that BIANCA, which will be freely available as part of the FSL package, is a reliable method for automated WMH segmentation in large cross-sectional cohort studies.

303 citations

Journal ArticleDOI
TL;DR: The criteria that should be satisfied by a descriptor for nonrigid shapes with a single closed contour are discussed and a shape representation method that fulfills these criteria is proposed that is very efficient and invariant to several kinds of transformations.
Abstract: In this paper, we discuss the criteria that should be satisfied by a descriptor for nonrigid shapes with a single closed contour. We then propose a shape representation method that fulfills these criteria. In the proposed approach, contour convexities and concavities at different scale levels are represented using a two-dimensional (2-D) matrix. The representation can be visualized as a 2-D surface, where "hills" and "valleys" represent contour convexities and concavities, respectively. The optimal matching of two shape representations is achieved using dynamic programming and a dissimilarity measure is defined based on this matching. The proposed algorithm is very efficient and invariant to several kinds of transformations including some articulations and modest occlusions. The retrieval performance of the approach is illustrated using the MPEG-7 shape database, which is one of the most complete shape databases currently available. Our experiments indicate that the proposed representation is well suited for object indexing and retrieval in large databases. Furthermore, the representation can be used as a starting point to obtain more compact descriptors.

263 citations