Automatic detection of abnormal vascular cross-sections based on density level detection and support vector machines.
OBJECTIVE
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.


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.
Zuluaga
Maria A
MA
Grupo Imagine, Grupo de Ingeniería Biomédica, Universidad de los Andes, Bogotá, Colombia. ale-zulu@uniandes.edu.co
Magnin
Isabelle E
IE
Hernández Hoyos
Marcela
M
Delgado Leyton
Edgar J F
EJ
Lozano
Fernando
F
Orkisz
Maciej
M
D000465:Algorithms; D001185:Artificial Intelligence; D003327:Coronary Disease / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional; D010363:Pattern Recognition, Automated; D019047:Phantoms, Imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed