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The ultrasonic technique is an indispensable imaging modality for the diagnosis of breast cancer in young women due to its ability to efficiently capture the tissue properties and decrease the negative recognition rate, thereby avoiding non-essential biopsies. Despite the advantages, ultrasound images are affected by speckle noise, generating fine-false structures that decrease the contrast of the images and diminish the actual boundaries of tissues in the ultrasound image. Moreover, speckle noise negatively impacts the subsequent stages of the image processing pipeline, such as edge detection, segmentation, feature extraction, and classification. Previous studies have formulated various speckle reduction methods for ultrasound images; however, these methods suffer from being unable to retain finer edge details; they also require more processing time. In this study, we propose a breast ultrasound de-speckling method based on rotationally invariant block matching non-local means (RIBM-NLM) filtering. The effectiveness of our method is demonstrated by comparing our results with those for three established de-speckling techniques: the switching bilateral filter (SBF), the non-local means filter (NLMF), and the optimized non-local means filter (ONLMF). We analyzed 250 images from a public dataset and six images from a private dataset. Evaluation metrics, including the Self-Similarity Index Measure (SSIM), the Peak Signal-to-Noise Ratio (PSNR), and the Mean Square Error (MSE), were utilized to measure performance. With the proposed method, we were able to record an average SSIM of 0.8915, a PSNR of 65.97, an MSE of 0.014, an RMSE of 0.119, and a computational speed of 82 s at a noise variance of 20 dB using the public dataset, all with a p-value of less than 0.001 compared against NLMF, ONLMF, and SBF. Similarly, the proposed method achieved an average SSIM of 0.83, a PSNR of 66.26, an MSE of 0.015, an RMSE of 0.124, and a computational speed of 83 s at a noise variance of 20 dB using the private dataset, all with a p-value of less than 0.001 compared against NLMF, ONLMF, and SBF.

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Purpose In a clinical setting, blood oxygen saturation is one of the most important vital sign indicators. A pulse oximeter is a device that measures the blood oxygen saturation and pulse rate of patients with various disorders. However, due to ethical concerns, commercially available pulse oximeters are limited in terms of calibration on critically sick patients, resulting in a significant error rate for measurement in the critical oxygen saturation range. The device’s accessibility in developing countries’ healthcare settings is also limited due to portability, cost implications, and a lack of recognized need. The purpose of this study was to develop a reliable, low-cost, and portable pulse oximeter device with improved accuracy in the critical oxygen saturation range. Methods The proposed device measures oxygen saturation and heart rate using the reflectance approach. The rechargeable battery and power supply from the smartphone were taken into account, and the calibration in critical oxygen saturation values was performed using Prosim 8 vital sign simulator, and by comparing with a standard pulse oximeter device over fifteen iterations. Results The device’s prototype was successfully developed and tested. Oxygen saturation and heart rate readings were both accurate to 97.74% and 97.37%, respectively, compared with the simulator, and an accuracy of 98.54% for the measurement of blood oxygen saturation was obtained compared with the standard device. Conclusion The accuracy of oxygen measurement attained in this study is significant for measuring oxygen saturation for patients in critical care, anesthesia, pre-operative and post-operative surgery, and COVID-19 patients. The advancements made in this research have the potential to increase the accessibility of pulse oximeter in resource limited areas.

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