Preprocessing for improved computer aided detection in medical ultrasound

The Role of Affordable, Point-of-Care Technologies for Cancer Care in Low- and Middle-Income Countries: A Review and Commentary

“…Supplementing innovations in hardware by layering on disease-specific, image analysis tools provides an opportunity for improved cancer care in resource-limited settings by decreasing the cost and expertise required for accurate cancer diagnoses and staging [16] , [17] . Continued enhancement of image analysis algorithms has decreased false positive rates and unnecessary biopsies, particularly in breast cancer diagnostics [7] , [18] . These tools complement a trend towards task-shifting in cancer care, enabling alternate sectors of the healthcare workforce such as primary care providers, nurses, or community health workers to share the workload of over-burdened specialists [19] , for example: Computer-based algorithms combine a human image reading with a machine learning methodology as a second reader to augment early detection strategies for breast cancer [20] .…”

Diagnosis of breast cancer lesion using ultrasound images, elastography, and Ki-67 protein cell proliferation index

“…In a study by Vanithamani et al (14), they proposed a new combined method to increase the visual quality of medical ultrasound images. Mammone et al (15) showed that to improve ultrasound images, median filtering, local averaging, and compression with resampling (CRS) methods are used, and this algorithm is used in preprocessor computer-aided devices. In a study by Gómez-Flores et al (16), to improve the elastography images and reduce the artifact, they used the ring method of artifact attenuation.…”