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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The liver is considered as one of the complicated organs in human body. It has close proximity to the neighboring organs in abdomen with numerous anatomical variations. It is difficult to find out the severity of disease connected to the liver unless the scanned image is subjected to segmentation process. The difficulty level also varies with the diseases that affect the liver. Any of the disease alters its density, homogeneity, color and texture. Liver image segmentation is necessary to identify the complexity and severity of the disease and it remains as an open challenge to researchers. Among all liver segmentation algorithms, threshold segmentation is fastest, simplest and numerically less complex. The accuracy of threshold-based segmentation lies in the selection of threshold values which separates foreground and background. This paper proposes a novel multi-threshold liver segmentation model based on “Slope Difference Distribution” (SDD) of image histogram. It consists of three stages. In the first stage, the noise in Computed Tomography (CT) scan image is reduced using a median filter. In the second stage, automatic threshold values are obtained from SDD of image histogram. These threshold values separate the liver image accurately from abdominal CT scan image. In the third stage, seed points are selected automatically which grow outwardly using the Fast Marching Method (FMM) discovering liver border in CT scan image. The proposed model is tested on 55 CT scan images and it is providing satisfactory results.

Threshold-Based New Segmentation Model to Separate the Liver from CT Scan Images