Postdoctoral Position in Image Analysis for Melanoma Diagnosis At the French National Institute for Scientific Research (CNRS) Location and institutions:

Postdoctoral Position in Image Analysis for Melanoma Diagnosis

At the French National Institute for Scientific Research (CNRS)

Location and institutions: Ecole Nationale Supérieure des Mines, Saint-Etienne, FRANCE Institut Mines-Telecom - LGF, UMR CNRS 5307

Duration: The position is available from 2013 April 1

and is funded for 18 months.

Salary: The recipient will receive a net (free of taxes) income around 2000 euros/month (grant funded by the French National Research Agency - ANR). It will be paid by the CNRS through the LGF, UMR CNRS 5307.

Required qualifications:

• Candidates should have a Ph.D. (or equivalent qualification) in applied mathematics or computer sciences, focusing on image processing and analysis or very closely related areas;

• Programming skills with Matlab and C/C++;

• Knowledge in color image processing and analysis is highly desirable.

• A good level of written and spoken English (all the members of the team speak fluently in English).

Application procedure:

• Candidates should send an application letter with a PDF detailed CV and diploma photocopies, together with a list of publications, a PDF copy of their PhD thesis and at least two reference letters.

Documents should be sent at: [email protected]

• Deadline: 2013 January 31

Information:

For more information, please contact:

Dr. Johan D

Ecole Nationale Supérieure des Mines - 158 cours Fauriel - 42023 Saint-Etienne, FRANCE Email: [email protected] ; Phone number: (+33) (0) 4 77 42 02 19

http://www.mines-stetienne.fr/en ; http://www.mines-stetienne.fr/~debayle/

Research Topic: The topic proposed for this postdoctoral position is focused on the quantitative study of dermoscopic images for melanoma (skin cancer) diagnosis [1] with the help of color image processing and analysis. This subject is a part of the funded ANR project DIAMELA (Ugly duckling feature for melanoma detection: a validation study).

This topic will be addressed in the context of the GANIP (General Adaptive Neighborhood Image Processing) [2] approach which is a mathematical framework for nonlinear processing and analysis of gray-tone images. An image is represented by a set of spatially adaptive neighborhoods defined for each point of the image to be studied. Thereafter, these neighborhoods can be characterized [3] by geometrical, morphometrical or textural measurements providing image quantification without any segmentation step.

The objective of this subject is then to extend the GANIP approach to color images for quantifying and classifying dermoscopic images for detecting melanoma.

Related references:

[1] A. Tenenhaus, A. Nkengne, J.F. Horn, C. Serruys, A. Giron, B. Fertil. Detection of melanoma from dermoscopic images of naevi acquired under uncontrolled conditions. Skin Research and Technology, 16(1):

85-97, 2010.

[2] J. Debayle and J.C. Pinoli. Theory and Applications of General Adaptive Neighborhood Image Processing.

Advances in Imaging and Electron Physics, 167: 121-183, 2011.

[3] S. Rivollier, J. Debayle, and J.C. Pinoli. Integral Geometry and General Adaptive Neighborhood for

Multiscale Image Analysis. International Journal of Signal and Image Processing, 1(3): 141-150, 2010.