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The liver vessels, which have low signal and run next to brighter bile ducts, are difficult to segment from MR images. This study presents a fully automated and adaptive method to segment portal and hepatic veins on magnetic resonance images. In the proposed approach, segmentation of these vessels is achieved in four stages: (i) initial segmentation, (ii) refinement, (iii) reconstruction, and (iv) post-processing. In the initial segmentation stage, k-means clustering is used, the results of which are refined iteratively with linear contrast stretching algorithm in the next stage, generating a mask image. In the reconstruction stage, vessel regions are reconstructed with the marker image from the first stage and the mask image from the second stage. Experimental data sets include slices that show fat tissues, which have the same gray level values with vessels, outside the margin of the liver. These structures are removed in the last stage. Results show that the proposed approach is more efficient than other thresholding-based methods. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

Fat accumulation in the liver causes metabolic diseases such as obesity, hypertension, diabetes or dyslipidemia by affecting insulin resistance, and increasing the risk of cardiac complications and cardiovascular disease mortality. Fatty liver diseases are often reversible in their early stage; therefore, there is a recognized need to detect their presence and to assess its severity to recognize fat-related functional abnormalities in the liver. This is crucial in evaluating living liver donors prior to transplantation because fat content in the liver can change liver regeneration in the recipient and donor. There are several methods to diagnose fatty liver, measure the amount of fat, and to classify and stage liver diseases (e.g. hepatic steatosis, steatohepatitis, fibrosis and cirrhosis): biopsy (the gold-standard procedure), clinical (medical physics based) and image analysis (semi or fully automated approaches). Liver biopsy has many drawbacks: it is invasive, inappropriate for monitoring (i.e., repeated evaluation), and assessment of steatosis is somewhat subjective. Qualitative biomarkers are mostly insufficient for accurate detection since fat has to be quantified by a varying threshold to measure disease severity. Therefore, a quantitative biomarker is required for detection of steatosis, accurate measurement of severity of diseases, clinical decision-making, prognosis and longitudinal monitoring of therapy. This study presents a comprehensive review of both clinical and automated image analysis based approaches to quantify liver fat and evaluate fatty liver diseases from different medical imaging modalities., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Objectives: To assess performance of 16-multidetector computed tomography for small bowel obstruction with surgery as standard of reference. To assess the impact of coronal reformats on reader confidence, and to address management perspective and surgeon's assessment of coronal images., Materials and Methods: 16-Multidetector computed tomography scans of 30 patients presenting with clinical features of small bowel obstruction were reviewed. Five-millimeter axial images and 2.5-mm coronal images were available for interpretation. All patients had subsequent surgery. Two blinded readers independently reviewed axial and then coronal and axial (combination) images for transition site, etiology, and complications. Reader confidence was scored on a 3-point scale. A single surgeon evaluated studies for adequacy of scans and usefulness of coronal images., Results: Results showed that the recorded accuracies were slightly higher for etiology, transition site, and complications using the combination data set; this reached statistical significance for etiology only (P = 0.08). There was no significant increase in scan evaluation time with addition of coronal images. Surgeon considered the coronal images more informative as compared with the axial images in 76.6% of cases., Conclusions: Coronal images generated at the scanner console are complementary to axials and improve reader confidence. Surgeons find coronal images more helpful than axial images for management.