**5. Giant cell arteritis detection using medical image processing and analysis**

Medical imaging is a commonly used method for detecting GCA and the diagnosis of arteries related diseases. Nowadays, medical image processing and analysis methods are used to facilitate the identification of the boundaries of internal organs from medical images and thus enhance the diagnostics of specific abnormalities. Patients with GCA may be indicated for medical imaging examination for initial diagnosing or monitoring of the disease activities.

GCA detection can be defined as the procedure in which the GCA region of interest can be detected and identified from medical images. In clinical application, the GCA diagnostic planning is defined as the process in which it is planned, using the computer system, where the GCA disease can be detected and quantified.

GCA detection passes through two main procedures: the pre-processing phase and the processing and analysis phase. The pre-processing phase improves and enhances the quality of an image after removing the noise, irrelevant and unwanted parts of the scanned image. The enhancement of image quality is obtained by the use of filtering techniques, removal of noise, and contrast enhancement methods. The processing phase includes all the steps of processing, which are used for identification, segmentation, measurement, and quantification of GCA.

GCA segmentation is composed of a series of image processing algorithms that depend on the medical image type and quality. The core image processing algorithms include:

a.The image enhancement and denoising algorithms:


b.Image processing and analysis that includes:


c.Region of interest post-processing that includes surface or volume reconstruction, measurements, deformation, and simplifications for clinical application. The measurements include the calculation of ROI area, volume, distances from organs, and other basic measurements and statistics (Mean, median, SD…etc).

The proposed method for detecting the GCA is shown in **Figure 1**. The image enhancement and the segmentation based on the threshold method are calculated from 2D MRI and US image slices. The US image shows the left temporal artery, and the MRI shows the right temporal right artery segmentation.

This approach of segmentation allows the semi-automatic detection of the outlines of the artery in the enhanced medical image. The methods of segmentation by the threshold, region growing, and interactive segmentation is commonly used in the literature. In this chapter, we tested the methods on two case studies using semi-automatic methods for detecting the GCA.

The semi-automatic segmentation is done by studying the histogram and the threshold analysis of the 2D US and MRI images. The histogram analysis is used to identify the pixel densities of all areas of interest. In this study, we assumed that there are differences calcification density distribution between the blood,

#### **Figure 1.**

*The flow chart GCA detection method.*


#### **Table 1.**

*The US and MRI histogram and statistical analysis.*

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

**Figure 2.**

*Pre-processing filters and algorithms for the enhancement of MRI image of a 50 years old female patient.*

artery wall, and the GCA region since the density of the GCA region is often lower than the blood and the normal wall densities. The image histogram analysis is summarized in **Table 1**. The temporal artery wall and the GCA wall thickening diameters were calculated in both images, the results in **Table 1** show an increase in the artery wall in both cases (160 mm and 88 mm). Furthermore, the GCA artery wall roundness was higher than the normal artery roundness in both cases (0.25 mm and 0.35 mm), respectively. The pixel density threshold analysis shows that there are few differences between the normal and GCA regions as indicated in **Table 1**.

In the pre-processing phase, various filtering and thresholding algorithms are applied successively to obtain the artery contour and boundary. This contour is separated and segmented into three contours (regions): the artery wall, the blood, and the abnormal region (GCA). Results in **Figure 2** show the two cases before and after image pre-processing.

The segmented regions were quantified and measured using the 3D slicer measurement and quantification tools.
