ABSTRACT
Immunohistochemistry for vascular network analysis plays a fundamental role in basic science, translational research and clinical practice. However, identifying vascularization in histological tissue images is time consuming and markedly depends on the operator's experience. In this study, we present "blood vessel detection-BVD", an automatic algorithm for quantitative analysis of blood vessels in immunohistochemical images. BVD is based on extraction and analysis of low-level image features and spatial filtering techniques, which do not require a training phase. BVD algorithm performance was comparatively evaluated on histological sections from three different in vivo experiments. Collectively, 173 independent images were analyzed, and the algorithm's results were compared to those obtained by human operators. The developed BVD algorithm proved to be a robust and versatile tool, being able to quantify number, area, and spatial distribution of blood vessels within all three considered histologic datasets. BVD is provided as an open-source application working on different operating systems. BVD is supported by a user-friendly graphical interface designed to facilitate large-scale analysis.
Subject(s)
Algorithms , Tissue Engineering , Humans , Image Processing, Computer-Assisted/methods , Immunohistochemistry , Neovascularization, PathologicABSTRACT
RF-inhomogeneity correction is a relevant research topic in the field of magnetic resonance imaging (MRI). A volume corrupted by this artifact exhibits nonuniform illumination both inside a single slice and between adjacent ones. In this work a bias correction technique is presented, which suppresses this artifact on MR volumes scanned from different body parts without any a priori hypothesis on the artifact model. Theoretical foundations of the method are reported together with experimental results and a comparison is presented with both the 2D version of the algorithm and other techniques that are widely used in MRI literature.
Subject(s)
Algorithms , Artifacts , Image Enhancement/methods , Image Interpretation, Computer-Assisted/methods , Imaging, Three-Dimensional/methods , Magnetic Resonance Imaging/methods , Bias , Reproducibility of Results , Sensitivity and SpecificityABSTRACT
Segmentation techniques based on active contours algorithm are widely used in medical imaging. Unfortunately, they require a lot of parameters to be used and this can represent an issue for those physicians with not much informatics skills. This paper proposes a software tool which allows to segment multiple slice can differ organ extraction setting a small number of parameters. Moreover, the tool offers the functionality to perform a multiple segmentation the same time, so that an entire volume composed by hundreds slices can be segmented.
Subject(s)
Image Processing, Computer-Assisted/methods , Tomography, X-Ray Computed/methods , Algorithms , Computer Graphics , Contrast Media/pharmacology , Diagnosis, Computer-Assisted , Humans , Imaging, Three-Dimensional , Models, Theoretical , Reproducibility of Results , Software , Software Design , User-Computer InterfaceABSTRACT
The aim of this work was to develop and employ software for the study of the common carotid artery and its branches in the neck. We first constructed geometric models of anatomic components, for the building of knowledge bases to be used in automatic feature extraction techniques from images. Then, we developed a software system characterised by three-dimensional visualisation, image processing functions, knowledge base and a programming language to describe classification processing by means of fuzzy logic. The software system was used to perform automatic reconstruction of three-dimensional models of carotid arteries from contiguous CT scans of the neck. These findings suggest that such software systems represent a useful help in the study of image based diagnosis of carotid arteries.
Subject(s)
Carotid Artery, Common/anatomy & histology , Imaging, Three-Dimensional/methods , Neck/blood supply , Software Design , Humans , Models, AnatomicABSTRACT
An artificial intelligence system, NEWCHEM, for the development of new oncology therapies is described. This system takes into account the most recent advances in molecular and cellular biology and in cell-drug interaction, and aims to guide experimentation in the design of new optimal protocols. Further work is being carried out, aimed to embody in the system all the basic knowledge of biology, physiopathology and pharmacology, to reason qualitatively from first principles so as to be able to suggest cancer therapies.
