Abstract
The most prominent research area in the field of advanced machine vision paradigm for medical imaging applications is brought by the content-based image retrieval (CBIR) technique. With the help of emerging medical imaging systems, a patient's medical background can be easily obtained in the form of digitized data such as X-rays, magnetic resonance imaging (MRI), computed tomography, ultrasound, nuclear imaging, and so on. In the past, radiologists manually analyzed the patient's health condition. Now, the medical imaging process provides better information and depiction of the different cases. However, the conventional techniques have created some controversies among the various literatures including insufficient feature set, high semantic gap, and computational time complexity. The methods we have used for content retrieval are gray-level cooccurrence matrix, local binary pattern, color cooccurrence matrix, and support vector machine. MRI data were used for the completion of texture and shape-based retrieval. On improvising the previously ordained results, an algorithm is proposed using semantic image retrieval-based CBIR by combining three-dimensional features. This chapter also emphasizes a detailed comparative analysis of various techniques with the proposed method.
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 callMore From: Advanced Machine Vision Paradigms for Medical Image Analysis
Disclaimer: 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.
