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An automatic method to enhance microcalcifications using Normalized Tsallis entropy

机译:使用归一化Tsallis熵增强微钙化的自动方法

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摘要

This paper presents a new approach to enhance the contrast of microcalcifications in mammograms using a fuzzy algorithm based on Normalized Tsallis entropy. In phase Ⅰ, image is fuzzified using Gaussian membership function. In Phase Ⅱ, using the non-uniformity factor calculated from local information, the contrasts of microcalcifications are enhanced while suppressing the background heavily. Some of the previous works are related to Shannon entropy and Tsallis entropy. This is the first time in literature to propose an enhancement algorithm using Normalized Tsallis entropy. Normalized Tsallis entropy has an extra parameter q. Our proposed work is completely automatic and q values are selected empirically. The proposed approach improves the detection process vastly. Without Normalized Tsallis entropy enhancement, detection of MCs is meager 80.21%Tps with 8.1 Fps, whereas after introduction of Normalized Tsallis entropy, the results have surged to 96.25% Tps with 0.803 Fps.
机译:本文提出了一种新的方法,可以使用基于归一化Tsallis熵的模糊算法来增强乳房X线照片中微钙化的对比度。在第一阶段,使用高斯隶属度函数对图像进行模糊处理。在阶段Ⅱ中,利用局部信息计算出的非均匀性因子,微钙化的对比度得到了增强,同时又严重地抑制了背景。先前的一些工作与香农熵和沙利斯熵有关。这是文献中首次提出使用归一化Tsallis熵的增强算法。归一化的Tsallis熵具有一个额外的参数q。我们提出的工作是完全自动的,并且根据经验选择q值。所提出的方法极大地改善了检测过程。如果不使用归一化的Tsallis熵增强,则在8.1 Fps的条件下,MC的检测仅为80.21%Tps,而在引入归一化的Tsallis熵之后,结果却飙升至0.83 Fps的96.25%Tps。

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