Open AccessBiomedical image restoration using machine learning GPU acceleration approach for precision improvement
Author(s) -
Bramah Hazela,
. Aarti,
Arika Khandelwal,
S. Soundararajan,
Ruchi Vyas,
A. Balaji
Publication year - 2022
Publication title -
international journal of health sciences (ijhs) (en línea)
Language(s) - English
Resource type - Journals
eISSN - 2550-6978
pISSN - 2550-696X
DOI - 10.53730/ijhs.v6ns3.5892
Subject(s) - imaging phantom , projection (relational algebra) , artificial intelligence , computer vision , computer science , principal component analysis , acceleration , tracking (education) , image (mathematics) , parameterized complexity , algorithm , nuclear medicine , physics , medicine , psychology , pedagogy , classical mechanics
Focusing the image in a single x-ray projection, an algorithm proposed for actual time volumetric image rebuilt and 3-dimensional location of the lump. Using the Principal Component Analysis (PCA) initialize the parameterized Deformation Vector Fields (DVF) of pulmonary movement. By adjusting the PCA coefficients, applied the DVF applied to optimize the reference image so that, the simulated projection of the rebuilt image matches the observed projection. The digital phantom & patient information was used to evaluate the technique. The phantom has an average relative image rebuilt error of 7.5 percent and a 3-dimensional location of the lump inaccuracy of 0.9 mm, correspondingly. The patient's location of the lump inaccuracy is less than 2 mm. On a GPU NVIDIA C1060, recreating a single volumetric image from every projection takes about 0.2 & 0.3 secs for patient and phantom. From a single image, clinical relevance could lead to reliable 3-dimensional lump tracking.