Automatic detection of abnormal vascular cross-sections based on density level detection and support vector machines
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The goal is to automatically detect anomalous vascular cross-sections to attract the radiologist’s attention to possible lesions and thus reduce the time spent to analyze the image volume.
Materials and methods
We assume that both lesions and calcifications can be considered as local outliers compared to a normal cross-section. Our approach uses an intensity metric within a machine learning scheme to differentiate normal and abnormal cross-sections. It is formulated as a Density Level Detection problem and solved using a Support Vector Machine (DLD-SVM). The method has been evaluated on 42 synthetic phantoms and on 9 coronary CT data sets annotated by 2 experts.
The specificity of the method was 97.57% on synthetic data, and 86.01% on real data, while its sensitivity was 82.19 and 81.23%, respectively. The agreement with the observers, measured by the kappa coefficient, was substantial (κ = 0.72). After the learning stage, which is performed off-line, the average processing time was within 10 s per artery.
To our knowledge, this is the first attempt to use the DLD-SVM approach to detect vascular abnormalities. Good specificity, sensitivity and agreement with experts, as well as a short processing time, show that our method can facilitate medical diagnosis and reduce evaluation time by attracting the reader’s attention to suspect regions.
KeywordsVascular disease Atherosclerosis Computed tomography Computer assisted diagnosis Support vector machines Density level detection
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