The multiscale top-hat tensor enables specific enhancement of curvilinear structures in 2D and 3D images


Alharbi S. S., Sazak C., Nelson C. J., Alhasson H. F., Obara B.

METHODS, vol.173, pp.3-15, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 173
  • Publication Date: 2020
  • Doi Number: 10.1016/j.ymeth.2019.05.025
  • Journal Name: METHODS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, EMBASE, MEDLINE, Veterinary Science Database
  • Page Numbers: pp.3-15
  • Keywords: Curvilinear structures, Image enhancement, Mathematical morphology, Top-hat, Tensor representation, Vesselness, Neuriteness, RETINAL VESSEL SEGMENTATION, BLOOD-VESSELS, MATHEMATICAL MORPHOLOGY, ORIENTATION RESPONSES, GAUSSIAN FILTER, WAVELET, DELINEATION, RANKING, LEVEL
  • Recep Tayyip Erdoğan University Affiliated: Yes

Abstract

Quantification and modelling of curvilinear structures in 2D and 3D images is a common challenge in a wide range of biomedical applications. Image enhancement is a crucial pre-processing step for curvilinear structure quantification. Many of the existing state-of-the-art enhancement approaches still suffer from contrast variations and noise. In this paper, we propose to address such problems via the use of a multiscale image processing approach, called Multiscale Top-Hat Tensor (MTHT). MTHT produces a better quality enhancement of curvilinear structures in low contrast and noisy images compared with other approaches in a range of 2D and 3D biomedical images. The proposed approach combines multiscale morphological filtering with a local tensor representation of curvilinear structure. The MTHT approach is validated on 2D and 3D synthetic and real images, and is also compared to the state-of-the-art curvilinear structure enhancement approaches. The obtained results demonstrate that the proposed approach provides high-quality curvilinear structure enhancement, allowing high accuracy segmentation and quantification in a wide range of 2D and 3D image datasets.