Image Segmentation with Complex Artifacts and Correction of Bias

Abstract

ChanVese (CV) model ultimately solves many image segmentation issues based on area. Nevertheless, this procedure does not succeed when the given images of any particular application are skewed by means of the objects (outliers) and lighting bias which compensate the real contrast values. Within a single operational energy, the following two points are implemented in this research work, firstly a complex artifact class that prohibits strength outliers of skewing the image segmentation, and then within Retinex type of procedure, which disintegrate the concerned image into a piece-constant structural element and a smooth biased part. The parameters of CV-segmentation then function only on the design, and only in regions that are not recognized as objects. The process of Segmentation is considered as parametric process using a phase-field, effectively reducing threshold dynamics. The proposed method on a compilation of representative images from various modalities representing artifacts and/or bias are mentioned in this work. This method is considered useful where image distortion prevents conventional CV segmentation of activity and where artifacts and bias are of particular concern in the application area of medical imaging, for instance the magnetic resonance imaging (MRI) modality, where identification of lesions and correction of bias area is most preferred.