Combining approaches for early diagnosis of breast diseases using thermal imaging

This paper presents a combination of efforts on the construction of a tool to help in the diagnosis by infrared breast images. It considers from the basic problem of image acquisition and storage in a database to the automatic extraction of the region of interest of each breast (left and right), the feature extraction, the decision and the limits of the diagnosis as well. The main objective of this study is the analysis of the viability of the use of the IR images for automatic detection of pathologies by texture symmetric analysis. Moreover numerical simulations and experimentations are developed in order to analyse the relation between the internal temperature of the breast and the temperature on the breast surface during the image acquisition.

[1]  Hairong Qi,et al.  Breast cancer identification through shape analysis in thermal texture maps , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[2]  C. Allain,et al.  Characterizing the lacunarity of random and deterministic fractal sets. , 1991, Physical review. A, Atomic, molecular, and optical physics.

[3]  Bryan F. Jones,et al.  A reappraisal of the use of infrared thermal image analysis in medicine , 1998, IEEE Transactions on Medical Imaging.

[4]  Kai-Sheng Hsieh,et al.  A robust algorithm for the fractal dimension of images and its applications to the classification of natural images and ultrasonic liver images , 2010, Signal Process..

[5]  Aura Conci,et al.  Characterizing the Lacunarity of Objects and Image Sets and Its Use as a Technique for the Analysis of Textural Patterns , 2006, ACIVS.

[6]  David T. Rohrbaugh,et al.  Lacunarity definition for ramified data sets based on optimal cover , 2003 .

[7]  Pinliang Dong,et al.  Lacunarity analysis of raster datasets and 1D, 2D, and 3D point patterns , 2009, Comput. Geosci..

[8]  D. Vanel The American College of Radiology (ACR) Breast Imaging and Reporting Data System (BI-RADS): a step towards a universal radiological language? , 2007, European journal of radiology.

[9]  James M. Keller,et al.  Texture description and segmentation through fractal geometry , 1989, Comput. Vis. Graph. Image Process..

[10]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[11]  E Y Ng,et al.  Numerical computation as a tool to aid thermographic interpretation , 2001, Journal of medical engineering & technology.

[12]  B. Wiecek,et al.  Advanced thermal, visual and radiological image processing for clinical diagnostics , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

[13]  R. O'Neill,et al.  Lacunarity indices as measures of landscape texture , 1993, Landscape Ecology.

[14]  Jonathan Penm,et al.  A subset polynomial neural networks approach for breast cancer diagnosis , 2007, Int. J. Electron. Heal..

[15]  K. Vinoy,et al.  A new measure of lacunarity for generalized fractals and its impact in the electromagnetic behavior of Koch dipole antennas , 2006 .

[16]  Hairong Qi,et al.  Detecting Breast Cancer from Thermal Infrared Images by Asymmetry Analysis , 2003 .

[17]  Hairong Qi,et al.  Asymmetry analysis in breast cancer detection using thermal infrared images , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[18]  Christophe L. Herry,et al.  Digital processing techniques for the assessment of pain with infrared thermal imaging , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[19]  E Y Ng,et al.  Statistical analysis of healthy and malignant breast thermography. , 2001, Journal of medical engineering & technology.

[20]  J.F. Head,et al.  Infrared imaging: making progress in fulfilling its medical promise , 2002, IEEE Engineering in Medicine and Biology Magazine.

[21]  A. Conci,et al.  Using Hurst Coefficient and Lacunarity to diagnosis early breast diseases , 2010 .

[22]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[23]  D. Parkinson,et al.  Bayesian Methods in Cosmology: Model selection and multi-model inference , 2009 .

[24]  Mahua Bhattacharya,et al.  Identification of tiny and large calcification in breast: a study on mammographic image analysis , 2010, Int. J. Bioinform. Res. Appl..

[25]  Ludwig Boltzmann,et al.  Ableitung des Stefan'schen Gesetzes, betreffend die Abhängigkeit der Wärmestrahlung von der Temperatur aus der electromagnetischen Lichttheorie , 1884 .

[26]  Paulo R. M. Lyra,et al.  Parametric analysis on the influences of tumor position and size in breast temperature profile , 2010 .

[27]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[28]  Qian Du,et al.  An improved box-counting method for image fractal dimension estimation , 2009, Pattern Recognit..

[29]  Ian Witten,et al.  Data Mining , 2000 .

[30]  Chih-Jen Lin,et al.  Working Set Selection Using Second Order Information for Training Support Vector Machines , 2005, J. Mach. Learn. Res..

[31]  Débora C. Muchaluat-Saade,et al.  Intelligent automated brain image segmentation , 2009 .

[32]  Gerald Schaefer,et al.  Thermography based breast cancer analysis using statistical features and fuzzy classification , 2009, Pattern Recognit..

[33]  E. Y.-K. Ng,et al.  A review of thermography as promising non-invasive detection modality for breast tumor , 2009 .

[34]  M. Prize,et al.  Automated image segmentation for breast analysis using infrared images , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[35]  Mahua Bhattacharya,et al.  Identification of microcalcifications and grading of masses using digital mammogram , 2010, Int. J. Medical Eng. Informatics.

[36]  Anselmo Cardoso de Paiva,et al.  Detection of masses in mammographic images using geometry, Simpson's Diversity Index and SVM , 2010 .

[37]  Walker H. Land,et al.  Multiclass primal Support Vector Machines for breast density classification , 2009, Int. J. Comput. Biol. Drug Des..

[38]  Xin Wang Learning the similarity in breast cancer tissue images , 2006 .

[39]  Yifan Chen,et al.  An overview of radar based ultra wideband breast cancer detection algorithms , 2010, Int. J. Ultra Wideband Commun. Syst..

[40]  W. Hargrove,et al.  Lacunarity analysis: A general technique for the analysis of spatial patterns. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[41]  Emilio Del-Moral-Hernandez,et al.  Cluster-based classification using self-organising maps for medical image databases , 2009 .

[42]  Jake K. Aggarwal,et al.  Boundary extraction in thermal images by edge map , 2004, SAC '04.

[43]  E. Yu,et al.  Functional infrared imaging of the breast , 2000, IEEE Engineering in Medicine and Biology Magazine.

[44]  Wei Guan,et al.  Aircraft recognition in infrared image using wavelet moment invariants , 2009, Image Vis. Comput..

[45]  Aura Conci,et al.  On Using Lacunarity for Diagnosis of Breast Diseases Considering Thermal Images , 2009, 2009 16th International Conference on Systems, Signals and Image Processing.

[46]  R. Simmons,et al.  Effectiveness of a noninvasive digital infrared thermal imaging system in the detection of breast cancer. , 2008, American journal of surgery.

[47]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[48]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[49]  Boguslaw Wiecek,et al.  Advanced Thermal Image Processing , 2006 .