Dr. Sharib Ali Profile

Dr. Sharib Ali

SPIE Involvement:

Author

Area of Expertise:

Image Registration , Segmentation , Image Mosaicking , Feature Extraction and Classification , Optical Flow , Deep learning based image analysis

Publications (4)

Proceedings Article | 20 March 2015 Paper

Robust bladder image registration by redefining data-term in total variational approach

Sharib Ali, Christian Daul, Ernest Galbrun, Marine Amouroux, François Guillemin, Walter Blondel

Proceedings Volume 9413, 94131H (2015) https://doi.org/10.1117/12.2077658

KEYWORDS: Bladder, Image registration, Cystoscopy, Motion estimation, Bladder cancer, Tumors, Algorithm development, Electroluminescent displays, Visualization, Motion models

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Proceedings Article | 18 March 2013 Paper

Automated detection of microaneurysms using robust blob descriptors

K. Adal, S. Ali, D. Sidibé, T. Karnowski, E. Chaum, F. Mériaudeau

Proceedings Volume 8670, 86700N (2013) https://doi.org/10.1117/12.2007913

KEYWORDS: Image quality, Image enhancement, Radon transform, Image contrast enhancement, Feature extraction, Image analysis, Computer aided diagnosis and therapy, Databases, Digital photography, Radon

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Proceedings Article | 13 March 2013 Paper

Steerable wavelet transform for atlas based retinal lesion segmentation

Sharib Ali, Kedir Adal, Désiré Sidibé, Edward Chaum, Thomas Karnowski, Fabrice Mériaudeau

Proceedings Volume 8669, 86693D (2013) https://doi.org/10.1117/12.2006357

KEYWORDS: Image segmentation, Eye, Macula, Gold, Optic nerve, Retina, Image processing, Image enhancement, Wavelet transforms, Principal component analysis

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Diabetic macular edema (DME) characterized by discrete white{yellow lipid deposits due to vascular leakage is one of the most severe complication seen in diabetic patients that cause vision loss in affected areas. Such vascular leakage can be treated by laser surgery. A regular follow{up and laser photocoagulation can reduce the risk of blindness by 90%. In an automated retina screening system, it is thus very crucial to make the segmentation of such hard exudates accurate and register these images taken over time to a reference co-ordinate system to make the necessary follow-ups more precise. We introduce a novel method of ethnicity based statistical atlas for exudates segmentation and follow-up. Ethnic background plays a significant role in retinal pigment epithelium, visibility of the choroidal vasculature and overall retinal luminance in patients and retinal images. Such statistical atlas can thus help to provide a solution, simplify the image processing steps and increase the detection rate. In this paper, bright lesion segmentation is investigated and experimentally verified for the gold standard built from African American fundus images. 40 automatically generated landmark points on the major vessel arches with macula and optic centers are used to warp the retinal images. PCA is used to obtain a mean shape of the retinal major arches (both lower and upper). The mean of the co-ordinates of the macula and optic disk center are obtained resulting 42 landmark points and together they provide a reference co-ordinate frame ( or the atlas co-ordinate frame) for the images. The retinal funds images of an ethnic group without any artifact or lesion are warped to this reference co-ordinate frame from which we obtain a mean image representing the statistical measure of the chromatic distribution of the pigments in the eye of that particular ethnic group. 400 images of African American eye has been used to build such a gold standard for this ethnic group. Any test image of the patient of that ethnic group is first warped to the reference frame and then a distance map is obtained with this mean image. Finally, the post-processing schemes are applied on the distance map image to enhance the edges of the exudates. A multi-scale and multi-directional steerable filters along with the Kirsch edge detector was found to be promising. Experiments with the publicly available HEI-MED dataset showed the good performance of the proposed method. We achieved the lesion localization fraction (LLF) of 82.5% at 35% of non{lesion localization fraction (NLF) on the FROC curve.

Proceedings Article | 16 February 2012 Paper

Synchronized slice viewing of similar image series

Sharib Ali, Antonio Foncubierta, Adrien Depeursinge, Fabrice Meriaudeau, Osman Ratib, Henning Müller

Proceedings Volume 8319, 83190K (2012) https://doi.org/10.1117/12.911233

KEYWORDS: Image registration, Lung, Brain, Neuroimaging, Cerebellum, Magnetic resonance imaging, Visualization, Image restoration, Computed tomography, Image analysis

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Comparing several series of images is not always easy as the corresponding slices often need to be selected manually. In times where series contain an ever-increasing number of slices this can mean manual work when moving several series to the corresponding slice. Particularly two situations were identified in this context: (1) patients with a large number of image series over time (such as patients with cancers that are monitored) frequently need to compare the series, for example to compare tumor growth over time. Manually adapting two series is possible but with four or more series this can mean loosing time. Having automatically the closest slice by comparing visual similarity also in older series with differing slice thickness and inter slice distance can save time and synchronize the viewing instantly. (2) analyzing visually similar image series of several patients can profit from being viewed in a synchronized way to compare the cases, so when sliding through the slices in one volume, the corresponding slices in the other volumes are shown. This application could be employed after content-based 3D image retrieval has found similar series, for example. Synchronized viewing can help finding or confirming the most relevant cases quickly. To allow for synchronized viewing of several image volumes, the test image series are first registered applying affine transformation for the global registration of images followed by diffeomorphic image registration. Then corresponding slices in the two volumes are estimated based on a visual similarity. Once the registration is finished, the user can subsequently move inside the slices of one volume (reference volume) and can view the corresponding slices in the other volumes. These corresponding slices are obtained after a correspondence match in the registration procedure. These volumes are synchronized in that the slice closest to the original reference volume is shown even when the slice thicknesses or inter slice distances differ, and this is automatically done by comparing the visual image content of the slices. The tool has the potential to help in a variety of situations and it is currently being made available as a plugin for the popular Osirix image viewer.

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