Energy enhanced tissue texture in spectral computed tomography for lesion classification

Texture Classification of Normal Tissues in Computed Tomography

Biomedical imaging is increasingly electronic in terms of image acquisition and display. Image detectors have become so sensitive that the amount of information acquired is greater than can be displayed at any one time without using special purpose hardware. Research in image processing methods is essential to fully exploit the information that has been acquired. Furthermore, the ability to ext...

Ridgelet-based Texture Classification of Tissues in Computed Tomography

The research presented in this article is aimed at the development of an automated imaging system for classification of tissues in medical images obtained from Computed Tomography (CT) scans. The article focuses on using ridgelet-based multi-resolution texture analysis. The approach consists of two steps: automatic extraction of the most discriminative texture features of regions of interest an...

Pixel-Based Texture Classification of Tissues in Computed Tomography

Previous research has been done to classify different tissues/organs of interest present in medical images, in particular in Computed Tomography (CT) images. Most of the research used the anatomical structure present in the images in order to classify the tissues. In this paper, instead of using the anatomical structure, we propose a pixel-based texture approach for the representation and class...

Correlation Between Tissue Densities in Computed Tomography and Three Different Cone-Beam Computed Tomography Units (In Vitro)

Background and Aim: Bone density is of great assistance in the selection of the proper implant site. The present study aimed to assess the correlation between tissue densities in computed tomography (CT) and three different cone-beam computed tomography (CBCT) units. Materials and Methods: In this descriptive study, a radiographic phantom consisting of a transparent polymethyl methacrylate (PM...

Contrast Enhanced Computed Tomography