TITLE

Integrating adaptive neuro-fuzzy inference system and local binary pattern operator for robust retinal blood vessels segmentation

AUTHOR(S)
Fathi, Abdolhossein; Naghsh-Nilchi, Ahmad
PUB. DATE
May 2013
SOURCE
Neural Computing & Applications;May2013 Supplement, Vol. 22, p163
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Automatic extraction of blood vessels is an important step in computer-aided diagnosis in ophthalmology. The blood vessels have different widths, orientations, and structures. Therefore, the extracting of the proper feature vector is a critical step especially in the classifier-based vessel segmentation methods. In this paper, a new multi-scale rotation-invariant local binary pattern operator is employed to extract efficient feature vector for different types of vessels in the retinal images. To estimate the vesselness value of each pixel, the obtained multi-scale feature vector is applied to an adaptive neuro-fuzzy inference system. Then by applying proper top-hat transform, thresholding, and length filtering, the thick and thin vessels are highlighted separately. The performance of the proposed method is measured on the publicly available DRIVE and STARE databases. The average accuracy 0.942 along with true positive rate (TPR) 0.752 and false positive rate (FPR) 0.041 is very close to the manual segmentation rates obtained by the second observer. The proposed method is also compared with several state-of-the-art methods. The proposed method shows higher average TPR in the same range of FPR and accuracy.
ACCESSION #
87661125

 

Related Articles

  • Image Segmentation by Gaussian Mixture Models and Modified FCM Algorithm. Kalti, Karim; Mahjoub, Mohamed // International Arab Journal of Information Technology (IAJIT);Jan2014, Vol. 11 Issue 1, p11 

    The Expectation Maximization (EM) algorithm and the clustering method Fuzzy-C-Meam (FCM) are widely used in image segmentation. However, the major drawback of these methods is their sensitivity to the noise. In this paper, we propose a variant of these methods which aim at resolving this...

  • A Framework for Computer Vision in Dynamical Scenes. Dong Zhang; Ping Li // International Review on Computers & Software;Nov2012, Vol. 7 Issue 6, p3133 

    This paper proposes a framework for computer vision in dynamical scenes. Traditional computer vision methods (e.g. background subtraction, camera pose estimation and Structure from Motion) usually have the assumption that the images, image sequences or videos are captured in static scenes. This...

  • Automatic Quick-Shift Segmentation for Color Images. Ibrahim, Abdelhameed; Salem, Muhammed; Ali, Hesham Arafat // International Journal of Computer Science Issues (IJCSI);May2014, Vol. 11 Issue 3, p122 

    This paper proposes an automatic quick-shift segmentation method using illumination invariant representation of natural color images. In practice, the quick-shift segmentation method is sensitive to the choice of parameters, thus a quick tuning by hand is not sufficient. The proposed method...

  • AUTOMATIC SEGMENTATION OF LUNG CT IMAGES BY CC BASED REGION GROWING. PRABIN, A.; VEERAPPAN, J. // Journal of Theoretical & Applied Information Technology;10/10/2014, Vol. 68 Issue 1, p63 

    Computer Aided Diagnosis (CAD) of CT lung image has been a revolutionary step in the early diagnosis of diseases present in the lung. Developing an efficient and robust algorithm for Lung computer tomography (CT) image segmentation has been a demanding area of growing research of interest during...

  • A Novel Method for Automatically Deciding Initial Contour of Level Set Segmentation. Zhuan Song; Xiaowei He; Hai Geng // International Journal of Advancements in Computing Technology;Sep2012, Vol. 4 Issue 17, p61 

    The image segmentation result of level set method is subject to appropriate initial contour and optimal configuration of controlling parameters, which require micromesh manual intervention. In this paper, an autonomous approach is proposed for deciding initial contour of level set, by amplifying...

  • A Robust Line Filter for Automatic X-ray/CT Image Segmentation. Shaohu Peng; Hyundo Nam; Yanfen Gan; Xiao Hu // Applied Mechanics & Materials;2014, Vol. 721, p783 

    Automatic segmentation of the line-like regions plays a very important role in the automatic recognition system, such as automatic cracks recognition in X-ray images, automatic vessels segmentation in CT images. In order to automatically segment line-like regions in the X-ray/CT images, this...

  • Face Contour Tracking Based on Mean Shift and GVF Snake. Zhiyu Zhou; Jianwei Wu; Weicheng Yang; Zefei Zhu // Information Technology Journal;2013, Vol. 12 Issue 11, p1884 

    GVF Snake algorithm can't extract an accurate image edge if the face has a large scale change. And it is difficult for GVF Snake algorithm to solve deep depression problem and the occlusion problem which is more complex. A method of extracting the contour of face is proposed in this study, which...

  • Robust Spectral Clustering Using Statistical Sub-Graph Affinity Model. Eichel, Justin A.; Wong, Alexander; Fieguth, Paul; Clausi, David A. // PLoS ONE;Dec2013, Vol. 8 Issue 12, p1 

    Spectral clustering methods have been shown to be effective for image segmentation. Unfortunately, the presence of image noise as well as textural characteristics can have a significant negative effect on the segmentation performance. To accommodate for image noise and textural characteristics,...

  • SUPERVISED IMAGE SEGMENTATION USING LOT. Suresh Kumar, B.; Shivakumar, B. L. // Journal of Engineering & Applied Sciences;Oct2014, Vol. 9 Issue 10, p1946 

    The image segmentation is used to change or simplify the image representation for the purpose of easy understanding or quicker analysis. Previously K-means classify images based on mean value of the groups formed with the help of centroids. FCM segments images based on the membership value and...

Share

Read the Article

Courtesy of VIRGINIA BEACH PUBLIC LIBRARY AND SYSTEM

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics