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Updated: Jun 12, 2026
Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
Published on: June 16, 2014
Automatic detection of abnormal vascular cross-sections based on density level detection and support vector machines
Maria A Zuluaga1, Isabelle E Magnin, Marcela Hernández Hoyos
1Grupo Imagine, Grupo de Ingeniería Biomédica, Universidad de los Andes, Bogotá, Colombia. ale-zulu@uniandes.edu.co
Purpose:
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.
Results:
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.
Conclusions:
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.
