Automatic evaluation of the image quality of a mammographic phantom

In this work a method has been developed to analyse the digital image quality of a mammographic phantom by means of automatic process techniques. The techniques used for the digital image treatment are standard techniques as the image thresholding to detect objects, the regional growing for pixels pooling and the morphological operator application to determine the objects shape and size, etc. This study allows the obtention of information about the phantom characteristics, that due to its small size and lowly contrast can be obtained very difficultly by direct observation. The final aim of this work is to obtain one or more parameters to characterize the reference phantom quality image in an objective way. These parameters will serve to compare images obtained at different mammographic centers and also, to study the temporal evolution of the image quality produced by determined mammographic equipment.

[1]  Ralph E. Jacobson,et al.  A Novel Approach to the Derivation of Expressions for Geometrical MTF in Sampled Systems , 1999, PICS.

[2]  K J Kearfott,et al.  Automated analysis of the American College of Radiology mammographic accreditation phantom images. , 1997, Medical Physics (Lancaster).

[3]  Robin N. Strickland,et al.  Wavelet transforms for detecting microcalcifications in mammograms , 1996, IEEE Trans. Medical Imaging.

[4]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[5]  L Costaridou,et al.  Quantifying image quality at breast periphery vs mammary gland in mammography using wavelet analysis. , 2001, The British journal of radiology.

[6]  D. Chakraborty,et al.  Computer analysis of mammography phantom images (CAMPI): an application to the measurement of microcalcification image quality of directly acquired digital images. , 1997, Medical physics.

[7]  K. J. Ray Liu,et al.  Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms , 1997, IEEE Transactions on Medical Imaging.

[8]  Joachim Dengler,et al.  Segmentation of microcalcifications in mammograms , 1991, IEEE Trans. Medical Imaging.

[9]  Nicolaos B. Karayiannis,et al.  Detection of microcalcifications in digital mammograms using wavelets , 1998, IEEE Transactions on Medical Imaging.

[10]  Ling Guan,et al.  A CAD System for the Automatic Detection of Clustered Microcalcification in Digitized Mammogram Films , 2000, IEEE Trans. Medical Imaging.

[11]  P. G. Tahoces,et al.  Computer-aided diagnosis: automatic detection of malignant masses in digitized mammograms. , 1998, Medical physics.

[12]  Nico Karssemeijer,et al.  Single and multiscale detection of masses in digital mammograms , 1999, IEEE Transactions on Medical Imaging.

[13]  Heinz-Otto Peitgen,et al.  Scale-space signatures for the detection of clustered microcalcifications in digital mammograms , 1999, IEEE Transactions on Medical Imaging.

[14]  D R Dance,et al.  Objective assessment of phantom image quality in mammography: a feasibility study. , 1998, The British journal of radiology.

[15]  Atam P. Dhawan,et al.  Analysis of mammographic microcalcifications using gray-level image structure features , 1996, IEEE Trans. Medical Imaging.