Feature based CT image registration of liver cancer

Computer-aided diagnostic systems (CADS) assist radiologists in classifying liver cancer using computed tomography (CT) images. To enhance diagnosis performance, image sequences are recorded at various time points in a single/multi-view format. Mutual information (MI) is a widely used medical image...

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Bibliographic Details
Published in:Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine Journal of engineering in medicine, 2022-01, Vol.236 (1), p.3-11
Main Authors: Krishan, Abhay, Mittal, Deepti
Format: Article
Language:English
Subjects:
Computed tomography
Delay time
Diagnosis
Diagnostic systems
Humans
Image classification
Image enhancement
Image registration
Liver
Liver cancer
Liver Neoplasms - diagnostic imaging
Medical imaging
Moving images
Tomography, X-Ray Computed
Tumors
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Summary:Computer-aided diagnostic systems (CADS) assist radiologists in classifying liver cancer using computed tomography (CT) images. To enhance diagnosis performance, image sequences are recorded at various time points in a single/multi-view format. Mutual information (MI) is a widely used medical image registration metric with a high rate of success, but it can result in misregistration due to a lack of spatial details. To address this issue and to establish anatomical correspondence between multi-phase CT images of the liver, a features-based technique is developed in this article. The proposed model uses fixed and moving images as inputs, with both images having the same dimensions. The registered images are the two images that differ in terms of their combinations/colors. In the output registered images, the tumor in the liver portion has classes with viewpoints. There is an appropriate way to view the tumor, and the output registered images should permit concluding that the registered image of the delayed phases, with a longer delay time, contains the most region portion within the output registered image. The detected and matched values are greater than the values of the feature outcomes. Having a large tumor provides valuable information in the presenting form for discussing the variation of the various phases and delayed testing results. And this will aid the radiologist in making an accurate diagnosis.
ISSN:0954-4119
2041-3033
DOI:10.1177/09544119211040290