Dr. Désiré Sidibé Profile

Dr. Désiré Sidibé

Assistant Professor at Univ de Bourgogne

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Publications (10)

SPIE Journal Paper | 14 November 2017

Machine vision for timber grading singularities detection and applications

Mohamad Mazen Hittawe, Désiré Sidibé, Ouadi Beya, Fabrice Mériaudeau

JEI, Vol. 26, Issue 06, 063015, (November 2017) https://doi.org/10.1117/12.10.1117/1.JEI.26.6.063015

KEYWORDS: Machine vision, Associative arrays, Defect detection, Feature extraction, Image segmentation, X-rays, X-ray imaging, Image classification, Visualization, Discrete wavelet transforms

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Proceedings Article | 24 March 2016 Paper

Classification of melanoma lesions using sparse coded features and random forests

Mojdeh Rastgoo , Guillaume Lemaître, Olivier Morel, Joan Massich, Rafael Garcia, Fabrice Meriaudeau, Franck Marzani, Désiré Sidibé

Proceedings Volume 9785, 97850C (2016) https://doi.org/10.1117/12.2216973

KEYWORDS: Melanoma, Associative arrays, Skin cancer, Machine learning, RGB color model, Chemical species, Cancer, CAD systems, Image processing, Image segmentation, Surface plasmons, Feature extraction, Optical inspection

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Proceedings Article | 24 March 2016 Paper

Classification of SD-OCT volumes for DME detection: an anomaly detection approach

S. Sankar, D. Sidibé, Y. Cheung, T. Wong, E. Lamoureux, D. Milea, F. Meriaudeau

Proceedings Volume 9785, 97852O (2016) https://doi.org/10.1117/12.2216215

KEYWORDS: Optical coherence tomography, Feature extraction, Visual process modeling, Macula, Principal component analysis, Eye, Retina, Machine learning, Data modeling, Expectation maximization algorithms, Computer aided diagnosis and therapy, Image processing, 3D modeling

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Proceedings Article | 30 April 2015 Paper

A boosting approach for prostate cancer detection using multi-parametric MRI

Guillaume Lemaitre, Joan Massich, Robert Martí, Jordi Freixenet, Joan Vilanova, Paul Walker, Désiré Sidibé, Fabrice Mériaudeau

Proceedings Volume 9534, 95340A (2015) https://doi.org/10.1117/12.2182772

KEYWORDS: Magnetic resonance imaging, Principal component analysis, Prostate cancer, Feature extraction, Cancer, Computer aided diagnosis and therapy, Computer aided design, Skin cancer, Prostate

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Proceedings Article | 30 April 2015 Paper

A machine vision based approach for timber knots detection

Mohamad Mazen Hittawe, Désiré Sidibé, Fabrice Mériaudeau

Proceedings Volume 9534, 95340L (2015) https://doi.org/10.1117/12.2182770

KEYWORDS: X-rays, Machine vision, X-ray imaging, Target detection, Image analysis, Optimization (mathematics), Acoustics, Velocity measurements, Laser scanners, Resistance

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Proceedings Article | 20 March 2015 Paper

Automatic discrimination of color retinal images using the bag of words approach

I. Sadek, D. Sidibé, F. Meriaudeau

Proceedings Volume 9414, 94141J (2015) https://doi.org/10.1117/12.2075824

KEYWORDS: Associative arrays, Visualization, Feature extraction, Computer aided diagnosis and therapy, Optical discs, Data modeling, Binary data, Retina, Computer aided design, CAD systems

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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 | 14 February 2012 Paper

A shape-based statistical method to retrieve 2D TRUS-MR slice correspondence for prostate biopsy

Jhimli Mitra, Abhilash Srikantha, Désiré Sidibé, Robert Martí, Arnau Oliver, Xavier Lladó, Soumya Ghose, Joan Vilanova, Josep Comet, Fabrice Meriaudeau

Proceedings Volume 8314, 83143M (2012) https://doi.org/10.1117/12.911176

KEYWORDS: Prostate, Biopsy, Magnetic resonance imaging, Image segmentation, 3D image processing, Tissues, Prostate cancer, Statistical methods, Magnetism, Shape analysis

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Proceedings Article | 19 January 2009 Paper

Automatic background generation from a sequence of images based on robust mode estimation

D. Sidibé, O. Strauss, W. Puech

Proceedings Volume 7250, 72500P (2009) https://doi.org/10.1117/12.810157

KEYWORDS: Statistical analysis, Image segmentation, Error analysis, Machine vision, Computer vision technology, Video surveillance, Video, Fuzzy logic, Pattern recognition, Surveillance

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Showing 5 of 10 publications

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