Abstract
Automatic segmentation of curvilinear objects in medical images plays an important role in the diagnosis and evaluation of human diseases, yet it is a challenging uncertainty for the complex segmentation task due to different issues like various image appearance, low contrast between curvilinear objects and their surrounding backgrounds, thin and uneven curvilinear structures, and improper background illumination. To overcome these challenges, we present a unique curvilinear structure segmentation framework based on oriented derivative of stick (ODoS) filter and deep learning network for curvilinear object segmentation in medical images. Currently, a large number of deep learning models emphasis on developing deep architectures and ignore capturing the structural features of curvature objects, which may lead to unsatisfactory results. In consequence, a new approach that incorporates the ODoS filter as part of a deep learning network is presented to improve the spatial attention of curvilinear objects. In which, the original image is considered as principal part to describe various image appearance and complex background illumination, the multi-step strategy is used to enhance contrast between curvilinear objects and their surrounding backgrounds, and the vector field is applied to discriminate thin and uneven curvilinear structures. Subsequently, a deep learning framework is employed to extract varvious structural features for curvilinear object segmentation in medical images. The performance of the computational model was validated in experiments with publicly available DRIVE, STARE and CHASEDB1 datasets. Experimental results indicate that the presented model has yielded surprising results compared with some state-of-the-art methods.
Abstract (translated)
URL
https://arxiv.org/abs/2301.07475
