Region of interest contrast measures

A survey of local image contrast measures is presented and a new contrast measure for measuring the local contrast of regions of interest is proposed. The measures validation is based on the gradual objective contrast decreasing on medical test images in both grayscale and color. The performance of the eleven most frequented contrast measures is mutually compared and their robustness to different types of image degradation is analyzed. Since the contrast measures can be both global, regional and local pixelwise, a simple way of adapting the contrast measures for regions of interest is proposed.

[1]  Huijun Gao,et al.  Mammography visual enhancement in CAD-based breast cancer diagnosis. , 2013, Clinical imaging.

[2]  Martin D. Levine,et al.  Dynamic Measurement of Computer Generated Image Segmentations , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Sos S. Agaian,et al.  Transform-based image enhancement algorithms with performance measure , 2001, IEEE Trans. Image Process..

[4]  J Gottesman,et al.  Symmetry and constancy in the perception of negative and positive luminance contrast. , 1984, Journal of the Optical Society of America. A, Optics and image science.

[5]  J. Kulikowski,et al.  Pattern and flicker detection analysed by subthreshold summation. , 1975, The Journal of physiology.

[6]  Xiaoming Liu,et al.  A Direct Image Contrast Enhancement Algorithm in the Wavelet Domain for Screening Mammograms , 2009, IEEE Journal of Selected Topics in Signal Processing.

[7]  Arianna Mencattini,et al.  Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing , 2008, IEEE Transactions on Instrumentation and Measurement.

[8]  Jaime S. Cardoso,et al.  INbreast: toward a full-field digital mammographic database. , 2012, Academic radiology.

[9]  Serge Miguet,et al.  Automatic detection of regions of interest in mammographies based on a combined analysis of texture and histogram , 2002, Object recognition supported by user interaction for service robots.

[10]  D. Tolhurst,et al.  Calculating the contrasts that retinal ganglion cells and LGN neurones encounter in natural scenes , 2000, Vision Research.

[11]  P. Whittle.  Increments and decrements: Luminance discrimination , 1986, Vision Research.

[12]  E. Peli,et al.  Perceived contrast in complex images. , 2012, Journal of vision.

[13]  L. Costaridou,et al.  A wavelet-based spatially adaptive method for mammographic contrast enhancement , 2003, Physics in medicine and biology.

[14]  Jeffrey Zheng,et al.  Extracting hidden visual information from mammography images using conjugate image enhancement software , 2005, 2005 IEEE International Conference on Information Acquisition.

[15]  Sos S. Agaian,et al.  Nonlinear Unsharp Masking for Mammogram Enhancement , 2011, IEEE Transactions on Information Technology in Biomedicine.

[16]  Petr Somol,et al.  Computer-Aided Evaluation of Screening Mammograms Based on Local Texture Models , 2009, IEEE Transactions on Image Processing.

[17]  Albert A. Michelson,et al.  Studies in Optics , 1995 .

[18]  P. Taylor,et al.  Impact of computer-aided detection prompts on the sensitivity and specificity of screening mammography. , 2005, Health technology assessment.

[19]  Vikrant Bhateja,et al.  Non-linear polynomial filters for edge enhancement of mammogram lesions , 2016, Comput. Methods Programs Biomed..

[20]  J. Salvado,et al.  Detection of calcifications in digital mammograms using wavelet analysis and contrast enhancement , 2005, IEEE International Workshop on Intelligent Signal Processing, 2005..

[21]  Sabine Dippel,et al.  Multiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform , 2002, IEEE Transactions on Medical Imaging.

[22]  A. Murat Tekalp,et al.  Performance measures for video object segmentation and tracking , 2003, IEEE Transactions on Image Processing.

[23]  Sos S. Agaian,et al.  Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy , 2007, IEEE Transactions on Image Processing.

[24]  Chun-Ming Chang,et al.  Coherence of multiscale features for enhancement of digital mammograms , 1999, IEEE Transactions on Information Technology in Biomedicine.

[25]  Jon Y. Hardeberg,et al.  Measuring perceptual contrast in digital images , 2012, J. Vis. Commun. Image Represent..

[26]  E. Peli.  Contrast in complex images. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[27]  Judy Czylok.  A SENSE OF TOUCH , 1979, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[28]  N. Diakides,et al.  Thermal infrared imaging in early breast cancer detection-a survey of recent research , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).