Comput Methods Programs Biomed - Medical imaging correction: a comparative study of five contrast and brightness matching methods.

Tópicos

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Resumo

Contrast and brightness matching are often required in many medical imaging applications, especially when comparing medical data acquired over different time periods, due to dissimilarities in the acquisition process. Numerous methods have been proposed in this field, ranging from simple correction filters to more complicated recursive techniques. This paper presents a comprehensive comparison of five methods for matching the contrast and brightness of medical image pairs, namely, Contrast Stretching, Ruttimann's Robust Film Correction, Boxcar Filtering, Least-Squares Approximation and Histogram Registration. The five methods were applied to a total of 100 image pairs, divided into five sets, in order to evaluate the performance of the compared methods on images with different levels of contrast, brightness and combinational contrast and brightness variations. Qualitative evaluation was performed by means of visual assessment on the corrected images as well as on digitally subtracted images, in order to estimate the deviations relative to the reference data. Quantitative evaluation was performed by pair-wise statistical evaluation on all image pairs in terms of specific features of merit based on widely used metrics. Following qualitative and quantitative analysis, it was deduced that the Histogram Registration method systematically outperformed the other four methods in comparison in most cases on average.

Resumo Limpo

contrast bright match often requir mani medic imag applic especi compar medic data acquir differ time period due dissimilar acquisit process numer method propos field rang simpl correct filter complic recurs techniqu paper present comprehens comparison five method match contrast bright medic imag pair name contrast stretch ruttimann robust film correct boxcar filter leastsquar approxim histogram registr five method appli total imag pair divid five set order evalu perform compar method imag differ level contrast bright combin contrast bright variat qualit evalu perform mean visual assess correct imag well digit subtract imag order estim deviat relat refer data quantit evalu perform pairwis statist evalu imag pair term specif featur merit base wide use metric follow qualit quantit analysi deduc histogram registr method systemat outperform four method comparison case averag

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