Estimating the body portion of CT volumes by matching histograms of visual words
作者： Johannes Feulner , S. Kevin Zhou , Sascha Seifert , Alexander Cavallaro , Joachim Hornegger
DOI: 10.1117/12.810240
关键词:
摘要: Being able to automatically determine which portion of the human body is shown by a CT volume image offers various possibilities like automatic labeling images or initializing subsequent analysis algorithms. This paper presents method that takes as input and outputs vertical coordinates its top and bottom slice in normalized coordinate system whose origin unit length are determined anatomical landmarks. Each described histogram visual words: Feature vectors consisting of an intensity SURF descriptor first computed on regular grid then classified into the closest words form histogram. The vocabulary quantization feature space offline clustering large number feature from prototype volumes into (or cluster centers) via K-Means algorithm. For set known the slice descriptions advance. test 1D rigid registration with axial direction. similarity two slices is measured comparing their histograms words. Cross validation dataset 44 proved the robustness results. Even for ca. 20cm height, average error was 15.8mm.
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