Aziz, Muhammad Haziq Abd (2025) Fabrication of angiography quality control phantom for image quality evaluation using machine learning. Project Report. Universiti Sains Malaysia. (Submitted)
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Abstract
Angiography's QC suffers from subjective evaluations and a lack of specialised phantoms. This study addresses this by developing an affordable, in-house angiography phantom and evaluating the image quality using a machine learning (ML) approach. Purpose: 1) Design and fabricate an in-house phantom for high contrast and spatial resolution; 2) Assess ML model performance and validation; 3) Validate the best ML for evaluation of phantom image quality. Method: An in-house phantom was 3D-printed using LW-PLA-HT, incorporating tungsten carbide beads for high contrast and a Huttner Type 18-line pair for spatial resolution. 14 angiographic images were acquired from HPUSM and analysed in MATLAB R2024a. Image analysis involved pre-processing, segmentation, feature extraction and augmentation were applied. Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) classifiers were evaluated using accuracy, precision, recall, F1-score, and AUC, with 10-fold cross-validation and an 80/20 training/testing. Results: Human evaluations showed variability. Among SVM, KNN, and RF, Random Forest demonstrated the best overall performance. For high-contrast image classification, RF achieved exceptional results (100% accuracy, 1.0000 F1 score), followed by KNN (76.11% accuracy, 0.7503 F1 score), and SVM (61.95% accuracy, 0.6095 F1 score). Spatial resolution classification was more challenging, with RF again leading (90.32% accuracy, 0.9050 F1 score), followed by KNN (64.52% accuracy, 0.6650 F1 score), and SVM (32.26% accuracy, 0.3180 F1 score). Conclusion: Random Forest demonstrated the best performance in this research, which highlights the viability of fabricating a cost-effective angiography phantom and utilising ML for image quality assessment.
| Item Type: | Monograph (Project Report) |
|---|---|
| Uncontrolled Keywords: | Angiography's |
| Subjects: | R Medicine R Medicine > RC Internal medicine > RC71-78.7 Examination. Diagnosis Including raiography |
| Divisions: | Kampus Kesihatan (Health Campus) > Pusat Pengajian Sains Kesihatan (School of Health Sciences) > Monograph |
| Depositing User: | MUHAMMAD AKIF AIMAN AB SHUKOR |
| Date Deposited: | 16 Oct 2025 06:36 |
| Last Modified: | 18 Nov 2025 01:23 |
| URI: | http://eprints.usm.my/id/eprint/63000 |
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