3D Cyst Sonomammogram Projection Using Reflection Coefficient and Mass Density in Python

The formation of a breast cyst is a common problem among females nowadays, and there is a possibility of developing a complex cyst that consists of fluid with a solid deposit. Appropriate identification can be achieved, nevertheless, by adapting life signs universally rather than just focusing on characteristics that signify regional responses to survival-related problems. The models for health statistics should take into account the chronological and ecological variables that are unique to each planetary and period of time. Thus, there is a non-invasive method needed to project a cyst’s pattern in 3D to calculate the cyst’s size. In this paper, a novel algorithm is used to project the segmented region extracted from ultrasound breast images through the calculation of the reflection coefficient as a threshold. Initially, the acquired images are binned twice, and the reflection coefficient is calculated for the second level bin. Then, for the extracted region of interest (RoI), the value of mass density is calculated, which has a range of 0.92–1.14 g/cm3. Then, the mapping between the reflection coefficient to mass density is performed to confirm the RoI in second-level binning. Finally, the segmented RoI is projected as a 3D pattern by considering the size of second-level bins on the X and Y axis and the range of reflection coefficient on the Z axis.