Retinal Blood Vessel Segmentation Using Gabor Filter and ...
An improved matched filter for blood vessel detection of digital retinal images
description
Transcript of An improved matched filter for blood vessel detection of digital retinal images
An improved matched filter for blood vessel detection of digital
retinal images
Source: Computers in Biology and Medicine (2007) pp. 262 – 267
Authors: Mohammed Al-Rawi, Munib Qutaishat, and Mohammed Arrar
Impact factor: 1.068 (2006)
Reporter: Kai Hung Chen
Date: Mar 11, 2008
Outline
Introduction
Proposed method
Experiment results
Introduction
(a) : The green band of a digital retina image
(b) : The profile of a one pixel width at the 200th row of the retina image shown in (a)
Proposed Method
2 2
exp, 2
2Lyxyxk
L: the length of the vessel segment that has the same orientation
σ: the spread of the intensity profile
Proposed Method
12 ..., ,2 ,1 wherecossin
sincos
iyxvupi
The point Pi that belongs to N is:
3 where, 2 , ,, TLuTuvuN
Neighborhood N is defined as:
Proposed Method
Npuyxk ii 2
exp, 2
2
The corresponding weights in the kernel i ( i =1, . . . , 12 which is the number of kernels) are given by:
.in points ofnumber theis
,,1 where,,,'
Na
yxkammyxkyxk iNpiiii i
The filter is normalized as:
Quality Factor
True pixels: pixels detected as vessels and they appear as vessels in the hand label image.
False pixels: pixels detected as vessels yet they appear as non-vessels in the hand labeled image.
true_ratio: divide the true pixels by the number of vessel pixels in the hand labeled image.
false_ratio: divide the false pixels by the number of non-vessel pixels in the hand labeled image.
Quality Factor ( QLσT ) = true_ratio − false_ratio
Experiment Results
Experiment Results
Experiment Results
MAA: maximum accuracy average, the average accuracy of all images.
Experiment Results
Experiment Results
Experiment Results
Experiment Results