English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Focal nodular hyperplasia (FNH), hepatocellular carcinoma (HCC) and cavernous hemangioma (HEM) are three types of solid focal liver lesions. Using CT images to identify these three types of lesions is the most commonly method. However, this method usually mainly depends on experiences. Whereas, more objective and quantitative image information could be explored with texture analysis method. This research aims to discuss the appreciations of texture analysis based on CT images for identifying FNH, HCC and HEM. This paper retrospectively analyzed 81 clinically or pathologically diagnosed cases, each of which contains contrast non-enhanced and contrast two-phasic enhanced CT images. The texture analysis was based on gray level co-occurrence matrix (GLCM) and wavelet transform. The results shows that the misclassification rates of texture classification were low to 2.73% between FNH and HEM (between benign lesions), 3.19% between FNH and HCC (between benign lesions and malignant lesions), and 1.67% between HEM and HCC (between benign lesions and malignant lesions) respectively. The effect of texture classification based on contrast two-phasic enhanced CT images was better than contrast non-enhanced CT images.

Textural Analysis of Liver Focal Lesions with Co-occurrence Matrix and Wavelet Transform on CT: A Feasible Study in FNH, HEM and HCC