Weighted Locality-Constrained Linear Coding for Lesion Classification in CT Images
Refereed conference paper presented and published in conference proceedings


Full Text

Times Cited
Web of Science3WOS source URL (as at 14/05/2021) Click here for the latest count

Other information
AbstractComputed tomography is a popular imaging modality for detecting abnormalities associated with abdominal organs such as the liver, kidney and uterus. In this paper, we propose a novel weighted locality-constrained linear coding (LLC) method followed by a weighted max-pooling method to classify liver lesions into three classes: cysts, metastases, hemangiomas. We first divide the lesions into same-size patches. Then, we extract the raw features in all patches followed by Principal Components Analysis (PCA) and apply K means to obtain a single LLC dictionary. Since the interior lesion patches and the boundary patches contribute different information in the image, we assign different weights on these two types of patches to obtain the LLC codes. Moreover, a weighted max pooling approach is also proposed to further evaluate the importance of these two types of patches in feature pooling. Experiments on 109 images of liver lesions were carried out to validate the proposed method. The proposed method achieves a best lesion classification accuracy of 96.33%, which appears to be superior compared with traditional image coding methods: LLC method and Bag-of-words method (BoW) and traditional features: Local Binary Pattern (LBP) features, uniform LBP and complete LBP, demonstrating that the proposed method provides better classification.
All Author(s) ListYuan YX, Hoogi A, Beaulieu CF, Meng MQH, Rubin DL
Name of Conference37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Start Date of Conference25/08/2015
End Date of Conference29/08/2015
Place of ConferenceMilan
Country/Region of ConferenceItaly
Journal nameConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Detailed descriptionorganized by IEEE,
Year2015
Month1
Day1
PublisherIEEE
Pages6362 - 6365
eISBN978-1-4244-9270-1
ISSN1557-170X
LanguagesEnglish-United Kingdom
KeywordsImage patch analysis; liver lesions classification; weighted LLC method; weighted max-pooling method

Last updated on 2021-15-05 at 02:00