شعاع سليم منور الحربي

الاسم الاول: 
شعاع
اسم العائلة: 
الحربي
الدرجة العلمية: 
دكتوراة
مجال الدراسة: 
العلوم والتقنية
المؤسسة التعليمية: 
Durham University

 

 

مجال التميز

تميز دراسي و بحثي

 

 

البحوث المنشورة

 

البحث (1):

 

عنوان البحث:

Sequential Graph-Based Extraction Of Curvilinear Structures

رابط إلى البحث:

Click here 

تاريخ النشر:

20/02/2019

موجز عن البحث:

In this paper, a new approach is proposed to extract an ordered sequence of curvilinear structures in images, capturing the largest and most influential paths first and then progressively extracting smaller paths until a prespecified size is reached. The results are demonstrated both quantitatively and qualitatively using synthetic and real-world images. The method is shown to outperform comparator methods for certain cases of noise, object class, and scale, while remaining fundamentally easier to use due to its low parameter requirement. 

 

 

البحث (2): 

 

عنوان البحث:

The Multiscale Top-Hat Tensor Enables Specific Enhancement Of Curvilinear Structures In 2D And 3D Images

 

رابط إلى البحث:

Click here 

تاريخ النشر:

07/06/2019

موجز عن البحث:

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.

 

 

المؤتمرات العلمية:

 

المؤتمر (1):

 

عنوان المؤتمر:

International Conference On Bioinformatics And Biomedicine (BIBM) 2018

تاريخ الإنعقاد:

03/12/2018

مكان الإنعقاد:

Madrid. Spain

طبيعة المشاركة:

Paper presentation

عنوان المشاركة:

Curvilinear Structure Enhancement By Multiscale Top-Hat Tensor In 2D/3D Images

ملخص المشاركة:

A wide range of biomedical applications require enhancement, detection, quantification and modelling of curvilinear structures in 2D and 3D images. Curvilinear structure enhancement is a crucial step for further analysis, but many of the enhancement approaches still suffer from contrast variations and noise. This can be addressed using a multiscale approach that produces a better quality enhancement for low contrast and noisy images compared with a single-scale approach in a wide range of biomedical images. Here, we propose the Multiscale Top-Hat Tensor (MTHT) approach, which combines multiscale morphological filtering with a local tensor representation of curvilinear structures in 2D and 3D images. The proposed approach is validated on synthetic and real data, and is also compared to the state-of-the-art approaches. Our results show that the proposed approach achieves high-quality curvilinear structure enhancement in synthetic examples and in a wide range of 2D and 3D images.

الرابط:

Click here

AttachmentSize
Shuaa Saleem Alharbi_atten (2).pdf822.77 KB