**3. GCA image processing and analysis**

Recently, the use of medical image processing and analysis tools improved GCA detection and diagnosis using medical imaging. These tools provide physicians with semi-automatic detection and quantification of suspected regions of interest and enhance GCA diagnosis.

Medical imaging processing refers to the process of digital imaging by using computer software. This process includes several types of techniques and operations such as image enhancement, segmentation, registration, and visualization [21].

The rapid advancement of image processing and analysis improved the medical care process in clinical applications.

The current advances in medical imaging made in medical fields such as imaging modalities, diagnostics, and treatment applications are designed on digital imaging technology processing and analysis. Medical image processing has been recognized as a source of innovation in the advanced medical care process including medical informatics, artificial intelligence, and bioinformatics. Recently, many libraries, tools, and software products can process and manipulate images from different modalities (CT, MRI, US, PET) and are available for clinical application and research purposes [22].

Medical image segmentation and classification algorithms are commonly used in clinical applications. Several automatic and semiautomatic segmentation methods were used for extracting veins and arteries on transverse and longitudinal medical images [23]. The deformable contour model, connected threshold, fast marching method, and many other methods were used for extracting the arteries from US images. Other studies reported the application of region growing, diffusion-based filter, edge detection combined with morphology methods, and Hough transforms [12, 23, 24]. In this chapter, we proposed several image processing and analysis algorithms for detecting and quantifying the GCA from patient medical images. The chapter introduced the connected threshold segmentation approach on two case studies for temporal arteritis using US and MRI imaging modalities extracted from the Radiopedia Dataset [12].

#### **4. GCA image processing and analysis software**

The GCA detection procedure was developed using the 3D Slicer Medical Imaging Interaction software as a fast prototyping open-source framework [25]. The 3D slicer is a free open-source software system providing extendibility by plug-ins development that interacts with the application core. It is an open-source software platform for medical image informatics, image processing, and threedimensional visualization. Built over two decades through support from the National Institutes of Health and a worldwide developer community, Slicer brings free, powerful cross-platform processing tools to physicians, researchers, and the general public.

The main focus of the 3D slicer is to enable the creation of highly interactive medical imaging software applications; it integrates different tools for medical imaging, computational modeling, computer graphics, deep learning, and numerical modeling for building applications with complex interaction mechanisms. It also provides a graphical user interface, multiple consistent views for the same data, 3D rendering, data retrieval, hierarchical organization for data objects, advanced visualization of multi-modal imaging, and support for 3D + t data.

3D slicer is an object-oriented cross-platform library implemented in C++ that supports Windows, Linux, and macOS. It integrates and extends widely-used opensource C++ libraries which are the Visualization Toolkit (VTK) and the Insight Toolkit (ITK), both supported by Kitware Inc. The ITK is an open-source, crossplatform library that provides an extensive suite of software algorithms for image analysis, it builds a set of fundamental algorithms especially for segmentation and registration. While the VTK supports a wide variety of visualization algorithms and advanced modeling techniques.

In addition to ITK and VTK, third-party packages can be integrated and used with MITK, such as the DICOM Toolkit (DCMTK, supported by Offis in Germany), and other commonly used C++ libraries (Boost, Qt, OpenCV among others). The

*Medical Image Processing and Analysis Techniques for Detecting Giant Cell Arteritis DOI: http://dx.doi.org/10.5772/intechopen.97161*

software includes numerous modules, extensions, datasets, pull requests, patches, issues report, suggestions—is made possible by users, developers, contributors, and commercial partners around the world. This development is funded by various grants and agencies [26].
