Image Processing

Medical Image Processing310912405023

G.SreejaProject Guide: Mr.S.Sadagopan

(Assistant Professor)(Computer Science and Engineering Department –

Jerusalem college of engineering)

OPTIC VESSEL AND DISK DETECTION IN RETINAL IMAGES

OBJECTIVE

The vessel detection and disc extraction from the retinal image using effective algorithm for analyzing the diabetic retinopathy severity.

Fast, Reliable and Efficient Optic Disc (OD) Localization

and segmentation are important tasks in automatic eye disease screening.

Optic Disc location arrived using Template Matching and the template is adaptable for different image resolutions.

By Initializing the Optic disc center and OD Radius, a Fast Hybrid Level set model combines the OD region and local gradient information to the segmentation of the disc boundary.

Morphological Filtering brings the removal of blood vessels and bright region other than Optic disc.

ABSTRACT

INTRODUCTION AND MOTIVATION

Analysis of the retinal image using reliable and efficient algorithm is necessary .

The analysis includes optic vessel and disc extraction effectively. Features taken from the retina gives the information about retinal abnormalities.

80% of the abnormalities includes for the eye is caused by diabetic retinopathy and glaucoma. So it is a major concern.

LITERATURE SURVEYTitle Method used Advantage Disadvantage Parameters used

H. Yu, Member et.al Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level set IEEE transactions on information technology in bio medicine Volume 16 No.4 2012

Localization and Segmentation using direct matched filtering and level sets

Robust and efficient No accuracy in advanced stage.Large milineated severe PPA.Constant Threshold value

Image Foot PrintRadius of optic diskOverlapping RatioMADHausdroff

Sandra Morales et.al Automatic Detection of Optic Disc Based on PCA &Mathematical MorphologyIEEE transactions on Medical imaging volume 32 No.4 2013.

PCA and Mathematical Morphology

Full automation of algorithmDoesn’t required clinical interference

Perfect Segmentation is difficult

Jaccards CoefficientDies AccuracyTPF and FPFMAD

Xiayu Xu et al Vessel Boundary Delineation on Fundus ImagesUsing Graph-Based Approach, IEEE transactions on Medical imaging volume 30, No.6 2011

Boundary delineationGraph based on approach

Few database closed to reference standardCould find retinal vessel boundary of even small vessels boundary

Constant value for thresholdSigma Value for constant hence not reliable

MeanStandard deviationGaussian Derivatives

LITERATURE SURVEY (Continuation)

Title Method used Advantage Disadvantage Parameters used

Marvin Tell Alonso et.al Edge Enhancement Algorithm Based on the Wavelet Transform for Automatic Edge Detection in SAR Images IEEE Transactions on GEO science and remote sensing volume 49, No.1 2011

Edge EnhancementWavelet transformationAutomatic edge detection

Robust and effective for applicationsNo Pre Filtering

UnsupervisedEdge Enhancement Critical

Hausdroff distancePfp PNF

Keith A. Go atman et al Detection of New Vessels on the OpticDisc Using Retinal PhotographsIEEE transactions on medical imaging volume 30 No 40 2011

Detection vessels New growing vessels were detected

Requires every possible referral features to be detectedReliably system to be safe

Ansari Bradley TestWater Shed TransformKthresh

Accurate extraction of vessel features so that we get

the finer details of vessels. Exact Optic disc location in case of severe cases.

PROBLEM STATEMENT

Usage of constant threshold value. Detection of vessels not accurate The methods used are not fast and reliable Perfect segmentation is difficult

DRAWBACKS OF CURRENT APPROACH

ISSUES GOING TO BE ADDRESSED

Exact location of optic disc Many features of vessels to be identified

ARCHITECTURE DIAGRAM

OPTIC DISC SIZE ESTIMATION

BACKGROUNDNORMALISATION

TEMPLATE MATCHING

DIRECTIONAL MATCHED FILTERING

MODEL PARAMETER OPTIMIZATION

SATURATION DETECTION IN RED

CHANNEL ROI

BLOOD VESSEL REMOVAL

BRIGHT REGION REMOVAL

FAST, HYBRID LEVEL SET SEGMENTATION

OPTIC DISC LOCALIZATION

LEAST SQUARE ELLIPSE FITTING

INPUT IMAGE

OD LOCATION

OPTIC DISC SEGMENTATION

OUTPUT IMAGE

EXTRACTION OF OPTIC VESSEL

AND ITS PARAMTER

OPTIMIZATION

AUTOMATIC OPTIC DISC

SIZE ITERATIVE METHOD

MODULES SPLIT UP

OPTIC DISC DETECTIONPreprocessing Template MatchingSegmentation Exact detection of Optic DiscDisc features extraction

OPTIC VESSEL DETECTIONPreprocessing Edge detection methodExact detection of Optic VesselVessel feature extraction

Background Normalization: To reduce the false detection of

OD candidates due to non-uniform illumination, we applied

an image illumination correction using Histogram

equalization method and we get an normalized image with

over smoothed background.

Template Matching: Is a binarization technique where the

image takes value 1 and background 0.Pearson coefficient

is used to get the values where values above 0.5 are taken

and rest are ignored. The value ranges from -1 o 1.The

formulae used to calculate is as follows

DATA FLOW DIAGRAM

BACK GROUND NORMAILISATION

TEMPLATE MATCHING

MATCHED FILERINGPEARSON COEIFFICENT

ESTIMATION

BINARIZATION

MATCHED FILERING

EXUDATES REMOVAL

REMOVAL OF FALSE POSITIVE

1DFD 2DFD

OD Segmentation:

In this stage, we first detect the saturation in the red channel of

the image.

In the red channel, the OD often appears with the most contrast

against the background, while vessels appear less prominently.

Thus, the OD segmentation algorithm is performed in red channel.

Then we remove the blood vessel from the retinal image because

Interference of blood vessels is one of the main difficulties in

accurate OD boundary segmentation.

In the OD segmentation ROI, areas containing features with bright

pigmentation, such as choroid vessels, exudates, and cotton wool

spots may interfere with the OD boundary segmentation.

We use morphological reconstruction to suppress the bright

regions that are lighter than their surroundings and are also

connected to the image border.

The new hybrid level set model uses speed function ,edge and

step function to get partial differential equation . The

threshold value is used to estimate region of interest which

depends on mean and standard deviation values.

Speed function is denoted as

The step and the edge function is used to get the narrow band region. The following equations are used.

The threshold value λ is used to get the region of interest. Where μ Mean c coefficient and σ is the standard deviation.

Exact Detection of optic discThe Disc curvature is used to define an internal force to make the evolving contour smooth during the hybrid level set model deformation. The final curve may still appear irregular due to the influence of strong blood vessels. To provide for a smooth contour, we fit the segmented OD boundary with an ellipse using the least-squares optimization. This step generates smooth OD borders that can be used for cup-to-disk ratio computation.

Vessel Feature ExtractionThe Features of the vessel such as segment length ,segment gradient ,direction of vessel growth are extracted. Features of normal and abnormal eyes are extracted.

Let F be the speed funcion which depends on local and global properties.Local properties = Normal direction and curvatureGlobal properties = shape, position and other independent external facts.To get the narrow band edge and step function are used.

We arrive at the curve evolution partial differential equation as follows

ALGORITHM – OPTIC VESSEL AND DISC DETECTION

∂ϕ∂t = gεk|∇ϕ| + β1(1 − λ)|∇ϕ| + β2∇g · ∇ϕ.

Based on additive operative approach the PDE equation

simplified as∂ϕ∂t = α(1 − λ) + β div(g∇ϕ)Λ – Threshold based on mean standard deviation and coefficientInitial threshold is set. Below he threshold values are ‘x’ and

above the threshold values are y.CalculationX / (total no of threshold) + Y / (total no of threshold) = zNew threshold = z/2Again The new threshold is calculated and region of interest is

arrived.

1] D. Pascolini and S. P.Mariotti, “Global estimates of visual impairment: 2010,” Br. J. Ophthalmol., pp. 614–621, 2011. [2] World Health Org., Action plan for the prevention of blindness and visual impairment 2009–2013 2012. [3] H. R. Taylor, “Eye care for the community,” Clin. Exp. Ophthalmol., vol. 30, no. 3, pp. 151–154, 2012. [4] T. Walter, J. C. Klein, P. Massin, and A. Erginay, “A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina,” IEEE Trans. Med. Imag., vol. 21, no. 10, pp. 1236–1243, Oct. 2012. [5] L. D. Hubbard, R. J. Brothers, W. N. King, L. X. Clegg, R. Klein, L. S. Cooper, A. Sharrett, M. D. Davis, and J. Cai, “Methods for evaluation of retinal microvascular abnormalities associated with hypertension/ sclerosis in the atherosclerosis risk in communities

References