The application of machine learning to radiological images is an increasingly active
research area that is expected to grow in the next five to ten years. Recent advances in …

The segmentation of the liver is a difficult process due to the changes in shape, border, and
density that occur in each section in computed tomography (CT) images. In this study, the …

Purpose The bag of visual words (BoVW) model is a powerful tool for feature representation
that can integrate various handcrafted features like intensity, texture, and spatial information …

Medical images play an important role in clinics. In most clinic sites, the diagnosis of
diseases and the comprehending of disease progression need firstly accurate interpretation …

Automatic liver lesion classification on computed tomography images is of great importance
to early cancer diagnosis and remains a challenging task. State-of-the-art liver lesion …

The spleen is one of the most frequently injured organs in blunt abdominal trauma.
Computed tomography (CT) is the imaging modality of choice to assess patients with blunt …

A content-based image retrieval (CBIR) system can support radiologists in making clinical
diagnosis through image analysis. Multiphase contrast-enhanced computer tomography …

Focal liver lesion classification is important to the diagnostics of liver disease. In clinics,
lesion type is usually determined by multi-phase contrast-enhanced CT images. Previous …

Current image retrieval techniques are mainly based on text or visual contents. However,
both text-based and contents-based methods lack the capability of utilizing human intuition …

Computer-aided diagnosis (CAD) systems have been verified to have the potential to assist
radiologists in clinical diagnosis to detect and characterize focal liver lesions (FLLs) based …