Software tools for medical diagnosis support automatic interpretation of digital X-ray films

The use of digital image processing as a medical diagnosis aid is now well established, being slowly but successfully integrated directly within the medical imaging devices. The traditional use of digital image processing as a post-imaging technique is still interesting, especially for new approaches to classical applications or for pioneering new ones. Our studies in the field of near automatic interpretation of particular X-ray types showed the possibility of performing various diagnostic-relevant tasks, such as grading the bone mass density based on the texture of the calcaneal bone, the analysis of the fit of un-cemented total hip prostheses and the investigation of the mammographic masses. The particular nature of the physics underlying the image acquisition in X-ray films suggested the use of new software tools based on a modified version of the classical Logarithmic Image Processing (LIP) model. We will show that the modified logarithmic operations allow, among other, fast and precise image enhancement and contour extraction in various situations.

[1]  Radhika Sivaramakrishna,et al.  Breast image registration techniques: a survey , 2006, Medical and Biological Engineering and Computing.

[2]  C. Florea,et al.  Logarithmic Type Image Processing Framework for Enhancing Photographs Acquired in Extreme Lighting , 2013 .

[3]  Alina Sultana,et al.  Automatic Tools for Diagnosis Support of Total Hip Replacement Follow-up , 2011 .

[4]  Corneliu Florea,et al.  PIECEWISE LINEAR APPROXIMATION OF LOGARITHMIC IMAGE PROCESSING MODELS FOR DYNAMIC RANGE ENHANCEMENT , 2009 .

[5]  J. Michel,et al.  Logarithmic image processing: additive contrast, multiplicative contrast, and associated metrics , 2001 .

[6]  Alan V. Oppenheim,et al.  Generalized Superposition , 1967, Information and Control.

[7]  R. Bird,et al.  Analysis of cancers missed at screening mammography. , 1992, Radiology.

[8]  M. Jourlin,et al.  Logarithmic Image Processing for Color Images , 2011 .

[9]  Alina Oprea,et al.  A Pseudo-logarithmic Image Processing Framework for Edge Detection , 2008, ACIVS.

[10]  Vasile Patrascu An Algebraical Model for Gray Level Images , 2014, ArXiv.

[11]  Jean-Charles Pinoli,et al.  Logarithmic Adaptive Neighborhood Image Processing (LANIP): Introduction, Connections to Human Brightness Perception, and Application Issues , 2007, EURASIP J. Adv. Signal Process..

[12]  Jean-Charles Pinoli,et al.  A model for logarithmic image processing , 1988 .

[13]  Hariton Costin,et al.  A Fuzzy Rules-Based Segmentation Method for Medical Images Analysis , 2013, Int. J. Comput. Commun. Control.