Robust MRI abnormality detection using background noise removal with polyfit surface evolution

Abstract

Image segmentation plays a vital role in MRI abnormality detection. This paper presents a robust MRI segmentation method to outline potential abnormality blobs. Thresholding and boundary tracing strategies are employed to remove background noises, and hence, the ROIs in the whole process are set. Subsequently, a polyfit surface evolution is proposed to approximately estimate bias field, which makes segmentation robust to image noises. Simultaneously, customized initial level set functions are devised so as to detect subtle bright and dark blobs which are highly potential abnormality regions. The proposed method improves bias field estimation and level set method to acquire fine segmentation with low computational complexities. Analysis of experimental results and comparisons with existing algorithms demonstrates that the proposed method can segment weak-edged, low-resolution MR brain images, and its performance prevails in accuracy and effectiveness.

Three-Dimensional Modeling of Spun-Bonded Nonwoven Meso-Structures

“…To ensure the 3D model is close to the real spatial structure of the nonwoven fabric, nonlinear regression was used to fit the curve function before establishing the model. This paper selected the Polyfit function [ 21 ] to carry out linear fitting and regression analysis, which is calculated by Equation (7): where Polyfit is solved by p = V / y . Nonlinear fitting was carried out by the Polyfit ( x , y , n ) function, and three-dimensional spatial coordinates were simplified, as shown in Figure 2 b.…”