Abstract
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. Recently, the use of medical image processing and analysis tools improved giant cell arteries (GCA) detection and diagnosis using patient specific medical imaging. 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 and region growing segmentation approaches on two case studies with temporal arteritis using ultrasound (US) and magnetic resonance imaging (MRI) imaging modalities extracted from the Radiopedia Dataset. The GCA detection procedure was developed using the 3D Slicer Medical Imaging Interaction software as a fast prototyping open-source framework. GCA detection passes through two main procedures: The pre-processing phase, in which we improve and enhances the quality of an image after removing the noise, irrelevant and unwanted parts of the scanned image by the use of filtering techniques, and contrast enhancement methods; and the processing phase which includes all the steps of processing, which are used for identification, segmentation, measurement, and quantification of GCA. The semi-automatic interaction is involved in the entire segmentation process for finding the segmentation parameters. The results of the two case studies show that the proposed approach managed to detect and quantify the GCA region of interest. Hence, the proposed algorithm is efficient to perform complete, and accurate extraction of temporal arteries. The proposed semi-automatic segmentation method can be used for studies focusing on three-dimensional visualization and volumetric quantification of Giant Cell Arteritis.
Highlights
Giant cell arteritis (GCA), called temporal arteritis or cranial arteritis is a systemic inflammation of medium to large-sized vessels. [1] The cause of the disease is currently unknown; autoimmunity is one hypothesis. [2] GCAGiant-Cell Arteritis most commonly occurs in females over the age of 50 years. [3] Temporal artery involvement classically presents with sudden onset of severe headache associated with inflammatory and ischemic symptoms; [1] GCA may involve other large-sized arteries, namely the aorta, subclavian, iliac, ophthalmic, occipital, and vertebral arteries, which have different presentation and may be involved independently from the cranial arteries. [4]Left untreated, GCA can lead to permanent visual loss and various systemic complications; there is a need for effective diagnosis
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
We discussed the use of medical image processing and analysis in detecting and quantification of GCA
Summary
Giant cell arteritis (GCA), called temporal arteritis or cranial arteritis is a systemic inflammation of medium to large-sized vessels. [1] The cause of the disease is currently unknown; autoimmunity is one hypothesis. [2] GCA. Giant cell arteritis (GCA), called temporal arteritis or cranial arteritis is a systemic inflammation of medium to large-sized vessels. [3] Temporal artery involvement classically presents with sudden onset of severe headache associated with inflammatory and ischemic symptoms; [1] GCA may involve other large-sized arteries, namely the aorta, subclavian, iliac, ophthalmic, occipital, and vertebral arteries, which have different presentation and may be involved independently from the cranial arteries. [5] The diagnosis mainly relies on clinical presentation, inflammatory markers (typically high erythrocyte sedimentation rate (ESR)), and usually histological confirmation by temporal artery biopsy. Temporal artery biopsy has been the standard test to confirm the diagnosis of GCA, which highly specific, is considered invasive and lacks sensitivity. [2, 6, 7] diagnosis of GCA often relies on the combination of clinical symptoms, serum inflammatory markers, and radiological imaging Temporal artery biopsy has been the standard test to confirm the diagnosis of GCA, which highly specific, is considered invasive and lacks sensitivity. [2, 6, 7] diagnosis of GCA often relies on the combination of clinical symptoms, serum inflammatory markers, and radiological imaging
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
