Improved Patch-Based Automated Liver Lesion Classification by Separate Analysis of the Interior and Boundary Regions

Idit Diamant, Assaf Hoogi, Christopher F. Beaulieu, Mustafa Safdari, Eyal Klang, Michal Amitai, Hayit Greenspan, Daniel L. Rubin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

The bag-of-visual-words (BoVW) method with construction of a single dictionary of visual words has been used previously for a variety of classification tasks in medical imaging, including the diagnosis of liver lesions. In this paper, we describe a novel method for automated diagnosis of liver lesions in portal-phase computed tomography (CT) images that improves over single-dictionary BoVW methods by using an image patch representation of the interior and boundary regions of the lesions. Our approach captures characteristics of the lesion margin and of the lesion interior by creating two separate dictionaries for the margin and the interior regions of lesions ('dual dictionaries' of visual words). Based on these dictionaries, visual word histograms are generated for each region of interest within the lesion and its margin. For validation of our approach, we used two datasets from two different institutions, containing CT images of 194 liver lesions (61 cysts, 80 metastasis, and 53 hemangiomas). The final diagnosis of each lesion was established by radiologists. The classification accuracy for the images from the two institutions was 99% and 88%, respectively, and 93% for a combined dataset. Our new BoVW approach that uses dual dictionaries shows promising results. We believe the benefits of our approach may generalize to other application domains within radiology.

Original languageEnglish
Pages (from-to)1585-1594
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Volume20
Issue number6
DOIs
StatePublished - Nov 2016

Funding

FundersFunder number
National Institutes of HealthU01-CA-142555
National Cancer InstituteU01CA190214
Tel Aviv University6997801

    Keywords

    • Automated diagnosis
    • classification
    • computed tomography
    • focal liver lesions
    • image patch analysis
    • visual words

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