Abstract Eggshell membrane is a kind of cheap, abundant resources with great potential utilization value. A variety of active ingredients in it can be used as raw materials for medicine, light industry and other industries. At present, soluble protein, collagen, keratin, sialic acid, hyaluronic acid and antimicrobial protein have been successfully extracted from eggshell membrane and exploited in various fields. This study aimed to separate the eggshell membrane for fully exploiting the potential utilization value of the eggshell, and reduce the amount of waste discharged, which is conducive to protecting the ecological environment on which we depend. In order to reduce energy consumption of eggshell membrane separation, separate eggshell membranes from eggshells by flash evaporation was investigated. The flash separation conditions were optimized considering three independent variables at three different levels by response surface methodology. The results showed that the processing parameters of separating eggshell membranes and eggshells with flash evaporation were 1.0 Mpa pressure, 45% moisture content, and 5 mm eggshells’ particle size. Under these conditions, the separation rate was 69.16%. Therefore, flash separation can be a promising approach for separating eggshells and eggshell membranes.

In this study, we have proposed a three-dimensional (3D) model reconstruction system for breast tumors. The proposed system can establish an accurate 3D model of tumors, which will serve as a diagnostic reference for physicians and also address the shortcomings of the traditional breast needle localization method and other localization methods reported in previous studies. This developed system uses multispectral breast magnetic resonance images as input and detects the contour of the tumor in different sections using an active contour method - multispectral gradient vector flow snake (MGVFS) method. Thus, the system constructs a 3D model of only the tumor is contained in a breast surface model and excludes other tissues. Since the accuracy of the reconstructed 3D model depends on the accuracy of the tumor contour detection, for confirming the results obtained with the MGVFS method, we conducted experiments to evaluate its accuracy in contour detection, and compared the results with those traditional contour detection methods. Our results demonstrate that the MGVFS method has the highest accuracy in contour detection, with a correct contour detection rate as high as 99.79%.