Content-based retrieval of focal liver lesions using bag-of-visual-words representations of single- and multiphase contrast-enhanced CT images.
This paper is aimed at developing and evaluating a content-based retrieval method for contrast-enhanced liver computed tomographic (CT) images using bag-of-visual-words (BoW) representations of single and multiple phases. The BoW histograms are extracted using the raw intensity as local patch descriptor for each enhance phase by densely sampling the image patches within the liver lesion regions. The distance metric learning algorithms are employed to obtain the semantic similarity on the Hellinger kernel feature map of the BoW histograms. The different visual vocabularies for BoW and learned distance metrics are evaluated in a contrast-enhanced CT image dataset comprised of 189 patients with three types of focal liver lesions, including 87 hepatomas, 62 cysts, and 60 hemangiomas. For each single enhance phase, the mean of average precision (mAP) of BoW representations for retrieval can reach above 90 % which is significantly higher than that of intensity histogram and Gabor filters. Furthermore, the combined BoW representations of the three enhance phases can improve mAP to 94.5 %. These preliminary results demonstrate that the BoW representation is effective and feasible for retrieval of liver lesions in contrast-enhanced CT images.
School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Wei
W
Yang
Zhentai
Z
Lu
Mei
M
Yu
Meiyan
M
Huang
Qianjin
Q
Feng
Wufan
W
Chen
D000465:Algorithms; D001185:Artificial Intelligence; D003287:Contrast Media; D006801:Humans; D008107:Liver Diseases / Q000000981:diagnostic imaging*; D008490:Medical Informatics / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011873:Radiology Information Systems; D036542:Tomography, Spiral Computed
D003287:Contrast Media