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Assessment of Diverse Quality Metrics for Medical Images including Mammography


T.Venkat, Narayana Rao


Vol. 14  No. 11  pp. 56-61


This paper presents the comparative analysis of various quality metrics for medical image processing. Measurement of image quality is vital for numerous image-processing applications. Image quality measurement is closely related to image resemblance assessment in which quality is based on the differences (or similarity) between a degraded image and the original, unmodified image chiefly in mammographic images. Simple verifiable techniques have been used for representing the image quality because we have employed automatic and mathematically defined procedure distinct detailed methods, which are costly, time consuming and observer dependent. In this paper images have been subjected to various degrees of blur, noise, compression and contrast levels and quality has been measured in terms of well known metrics such as Mean Squared Error (MSE), Structural Similarity Index Metrics (SSIM) Peak Signal-to-Noise Ratio (PSNR), Maximum difference (MD) including new measures of image qualities for low cost medical image analysis


Image quality analysis, Mean Square Error (MSE), Structural Similarity Index Metric(SSIM), Peak Signal to Noise Ratio(PSNR), Mean Absolute Error(MAE)