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A classic approach to obtain High Dynamic Range Images (HDRI) consists in combining multiple images of the same scene with varying exposures. However, if the scene is not static during the time of capture, moving objects will appear blurry and ghosted, i.e. in multiple locations. Detecting and removing ghosting artifacts is an important issue for automatic(More)
This paper tackles the difficult, but fundamental, problem of image matching under projective transformation. Recently, several algorithms capable of handling large changes of viewpoint as well as large scale changes have been proposed. They are based on the comparison of local, invariants descriptors which are robust to these transformations. However,(More)
This paper presents a novel method for non-rigid registration of transrectal ultrasound and magnetic resonance prostate images based on a non-linear regularized framework of point correspondences obtained from a statistical measure of shape-contexts. The segmented prostate shapes are represented by shape-contexts and the Bhattacharyya distance between the(More)
Accurate prostate segmentation in magnetic resonance (MR) images aids in volume estimation, surgical planing and multi-modal im­ age registration. However, automatic or semi-automatic prostate segmen­ tation in MR images is a challenging task due to inter-patient prostate shape and size variabilities. We propose a supervised learning framework of decision(More)
In this paper, we propose a simple method for the ghost detection problem in the context of merging multiple low dynamic range (LDR) images to form a high dynamic range (HDR) image. We show that the second biggest singular values extracted over local spatiotemporal neighbourhoods can be effectively used for ghost region detection. Furthermore, we combine(More)
This paper presents a method based on shape-context and statistical measures to match interventional 2D Trans Rectal Ultrasound (TRUS) slice during prostate biopsy to a 2D Magnetic Resonance (MR) slice of a pre-acquired prostate volume. Accurate biopsy tissue sampling requires translation of the MR slice information on the TRUS guided biopsy slice. However,(More)
Imaging artifacts in Transrectal Ultrasound (TRUS) images and inter-patient variations in prostate shape and size challenge computer-aided automatic or semi-automatic segmentation of the prostate. In this paper, we propose to use multiple mean parametric models derived from principal component analysis (PCA) of shape and posterior probability information to(More)
Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection(More)
Prostate segmentation is a challenging task, and the challenges significantly differ from one imaging modality to another. Low contrast, speckle, micro-calcifications and imaging artifacts like shadow poses serious challenges to accurate prostate segmentation in transrectal ultrasound (TRUS) images. However in magnetic resonance (MR) images, superior soft(More)
High dynamic range (HDR) image generation and display technologies are becoming increasingly popular in various applications. A standard and commonly used approach to obtain an HDR image is the multiple exposures fusion technique which consists of combining multiple images of the same scene with varying exposure times. However, if the scene is not static(More)