Design of Cost Efficient VBIR Technique Using ICA and IVCA

With an emphasis on early-stage contrast agent transit through tumour vasculature, this study presents Adaptive Complex Independent Components Analysis (ACICA) as a unique method for evaluating intravascular responsiveness in prostatic tissue. Furthermore, a new SVM clustering method is introduced t...

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Bibliographic Details
Published in:SN computer science 2024-06, Vol.5 (5), p.560, Article 560
Main Authors: Pradeep Kumar, B. P., Srinidhi, N. N., Shiva Darshan, S. L., Naresh, E.
Format: Article
Language:English
Subjects:
AI Based Internet of Healthcare: Analysis and Future Perspectives
Blood vessels
Cancer therapies
Cluster analysis
Clustering
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Contrast agents
Data Structures and Information Theory
Disease
Image analysis
Image contrast
Image databases
Image enhancement
Image retrieval
Independent component analysis
Information retrieval
Information seeking behavior
Information Systems and Communication Service
Magnetic resonance imaging
Measurement techniques
Medical diagnosis
Medical imaging
Original Research
Patients
Pattern Recognition and Graphics
Permeability
Pharmacokinetics
Prostate cancer
R&D
Radiation
Recall
Research & development
Retrieval
Software Engineering/Programming and Operating Systems
Time lag
Tomography
Tumors
Uniqueness
Vector quantization
Vision
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Summary:With an emphasis on early-stage contrast agent transit through tumour vasculature, this study presents Adaptive Complex Independent Components Analysis (ACICA) as a unique method for evaluating intravascular responsiveness in prostatic tissue. Furthermore, a new SVM clustering method is introduced that outperforms the conventional k-means clustering for image retrieval based on vision. The study emphasises how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may be improved in conjunction with quantitative analysis. Within the field of DCE-MRI, ACICA stands out as a unique intravascular attention measurer. Utilising the spatial independence of extravascular and intravascular magnetic resonance imaging (MR) data, ACICA offers a strong foundation for DCE-MRI image analysis. It incorporates pharmacokinetic modelling to optimise the time lag, especially useful for arterial curves, and a reference region (RR)-based technique to adjust the intravascular concentration curve. The model's evaluation yields outstanding results, with recall and accuracy ranging from 83 to 99% and 82.8% to 99.6%, respectively. The average recall and precision across datasets are 92.86% and 92.82%).All things considered, this study demonstrates the effectiveness of ACICA in evaluating intravascular responsiveness and presents viable paths for enhancing clinical results and diagnostic precision in the treatment of prostate cancer.
ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-024-02936-9