Computer methods and programs in biomedicine · 2026
Enhancing breast mass detection: Super-resolution multi-spectral transmission imaging with unstructured sinusoidal illumination.
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Liu Fulong, Zhou Xin, Wang Junqi
Аннотация
BACKGROUND AND OBJECTIVE: Optical transmission imaging of biological tissues is often hindered by blurring caused by light absorption and scattering, limiting its accuracy in detecting early-stage abnormalities such as breast tumors. This study aims to enhance the resolution and quality of multi-spectral transmission images by integrating unstructured sinusoidal illumination with super-resolution reconstruction techniques.
METHODS: Multi-spectral transmission images of tissue-mimicking phantoms were acquired at four wavelengths (435 nm, 546 nm, 700 nm, and 860 nm) under 3.5 Hz unstructured sinusoidal illumination. Images were extracted using Fast Fourier Transform (FFT) and combined into pseudo-color images. Six super-resolution methods-Locally Linear Embedding (LLE), Sparse Coding Super-Resolution (ScSR), Anchored Neighborhood Regression Super-Resolution (ANRSR), Super-Resolution Convolutional Neural Network (SRCNN), Fast Super-Resolution Convolutional Neural Network (FSRCNN), and Efficient Sub-Pixel Convolutional Neural Network (ESPCN)-were applied to enhance image quality. The enhanced images were evaluated using full-reference image quality assessment (FR-IQA) metrics, and heterogeneity detection accuracy was validated using the Faster-RCNN model.
RESULTS: The proposed combination significantly improved image quality across all metrics. Among the methods, LLE achieved the highest detection accuracy, with mean Average Precision (mAP) values of 97.57%, 98.26%, and 99.17% for detecting two, four, and seven types of heterogeneities, respectively. LLE also outperformed other methods in most FR-IQA metrics.
CONCLUSIONS: Integrating unstructured sinusoidal illumination with super-resolution reconstruction, particularly the LLE method, effectively produces high-quality multi-spectral transmission images. This approach holds strong potential for improving early breast cancer screening accuracy in clinical settings.
METHODS: Multi-spectral transmission images of tissue-mimicking phantoms were acquired at four wavelengths (435 nm, 546 nm, 700 nm, and 860 nm) under 3.5 Hz unstructured sinusoidal illumination. Images were extracted using Fast Fourier Transform (FFT) and combined into pseudo-color images. Six super-resolution methods-Locally Linear Embedding (LLE), Sparse Coding Super-Resolution (ScSR), Anchored Neighborhood Regression Super-Resolution (ANRSR), Super-Resolution Convolutional Neural Network (SRCNN), Fast Super-Resolution Convolutional Neural Network (FSRCNN), and Efficient Sub-Pixel Convolutional Neural Network (ESPCN)-were applied to enhance image quality. The enhanced images were evaluated using full-reference image quality assessment (FR-IQA) metrics, and heterogeneity detection accuracy was validated using the Faster-RCNN model.
RESULTS: The proposed combination significantly improved image quality across all metrics. Among the methods, LLE achieved the highest detection accuracy, with mean Average Precision (mAP) values of 97.57%, 98.26%, and 99.17% for detecting two, four, and seven types of heterogeneities, respectively. LLE also outperformed other methods in most FR-IQA metrics.
CONCLUSIONS: Integrating unstructured sinusoidal illumination with super-resolution reconstruction, particularly the LLE method, effectively produces high-quality multi-spectral transmission images. This approach holds strong potential for improving early breast cancer screening accuracy in clinical settings.