Study And Development Of Digital Image Processing Tool For Application Of Diabetic Retinopathy

Transcription

1 Study And Development O Digital Image Processing Tool For Application O Diabetic Retinopathy Name: Ms. Jyoti Devidas Patil mail ID: jyot.physics@gmail.com

2 Outline 1. Aims & Objective 2. Introduction 3. Classiication O Diabetic Retinopathy 4. MATLAB in Ophthalmology 5. Methodology & Process Flowchart 6. Social Impact & Challenges 7. Conclusion & Future Scope

3 Aim and Objective Aim- Aim o this research is to develop reliable and accurate image processing and pattern recognition methods to be used as an automatic tool or the mass screening o diabetic retinopathy and to aid ophthalmologist s diagnosis. Objectives- Recognize the importance o diabetic retinopathy as a public health problem To develop algorithms or detection o diabetic eye. To implement an automated detection tool o Diabetic Retinopathy using digital undus images. To extract and detect the eatures such as Micro aneurysms, Retinal Haemorrhages, Cotton wool spots, Hard exudates and Neovascular textures which will determine two general classiications: 1) Prolierative diabetic Retinopathy (abnormal (DR) eye). 2) Non Prolierative diabetic Retinopathy (normal) Study And Development O Digital Image Processing Tool For Application O Diabetic Retinopathy 3

4 Introduction Diabetic Retinopathy (DR): People with diabetes can have an eye disease like diabetic retinopathy, results in swelling and leaking o blood vessels. Sometimes abnormal new blood vessels grow on the retina. All o these changes can steal your vision. This research detects the presence o abnormalities in the retina using image processing techniques by applying morphological processing to the undus images to extract eatures such as blood vessels, micro aneurysms, haemorrhages,exudates and neo vascularization. Then depending on the Area o these eatures are used or the detection o severity o Diabetic Retinopathy. 4

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6 Tools used : 1. Median ilters, Image range ilter, Filtering, Gabor iltering 2. Threshold Techniques 3. Laplacian ilters 4.Otsu s Method 5. contrast enhance 6. Image segmentation 7. Image enhancement methods like Adaptive Contrast Enhancement, Histogram equalization. 8. Segmentation-color Space Selection, 9. Statistical Measures, 10. Fuzzy Gaussian Filter Tool, 11. Sobel and canny edge detection 12.Contrast Limited Adaptive Histogram Equalization (CLAHE) 13. Walter klein contrast enhancement

7 Diabetic Retinopathy Detection 7

8 Methodology Extract & detect the eatures such as blood vessels, micro-aneurysms & exudates, will determine classiication : normal or abnormal DR eye. Corresponding Steps : Read Input Image From Fundus Camera Image Pre-processing Anatomical Structure Extraction Feature Extraction Disease Severity Corresponding Treatment Maintaining Database & GUI 8

9 Methodology: WALTER KLEIN CONTRAST ENHANCEMENT This preprocessing method aims to enhance the contrast o undus images by applying a gray level transormation using the ollowing operator: ' ' max ' max min max ' min r ' min r Where {min,..., max}, {_ min,..., _ max} are the intensity levels o the original and the enhanced image, respectively, μ is the mean value o the original grayscale image and r R is a transition parameter. min max r r ' min, ' max,

10 Possible Outcome Original image is been process to get clear blood vessels & micro aneurysms

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12 Contrast Limited Adaptive Histogram Equalization (CLAHE) : CLAHE is very eective in making: the usually interesting salient parts more visible. Objective o Method : To deine a point transormation within a local airly large window with assumption that the intensity value within it is a stoical representation o local distribution o intensity value o the whole image. The local window is assumed to be unaected by the gradual variation o intensity between the image centers and edges. The point transormation distribution is localized around the mean intensity o the window and it covers the entire intensity range o the image. Consider a running sub image W o N X N pixels centered on a pixel P (i,j), the image is iltered to produced another sub image P o (N X N) pixels according to the equation below Where The image is split into disjoint regions, Apply local histogram equalization boundaries between the regions are eliminated with a bilinear interpolation.

13 maximum and minimum intensity values in the whole image, while and indicate the local window mean and standard deviation which are deined as: As a result o this adaptive histogram equalization, the dark area in the input image that was badly illuminated has become brighter in the output image while the side that was highly illuminated remains or reduces so that the whole illumination o the image is same.

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15 Result 5) : Result 6):

16 Challenges Diabetic Retinopathy in India Social Impact Expensive treatment, Poor metabolic control, Non-awareness Aordability : Cost eectiveness Rural population Lack o Lab. Facilities &Complex surgical procedures with Unpredictable outcome Inadequate acilities or diagnosis, investigation and management o DM Rural areas Vascular complications -No symptoms in stages amenable or treatment Patients can get treatment in early symptoms o Retinopathy. Patients will get ast detection results with aordable treatment. This project can improve the ability or early detection DR and their progress based on new imaging techniques. Ophthalmologists can judge image data. like an automated segmentation and thickness measurement o the nerve iber layer Available ophthalmologists are less Study And Development O Digital Image Processing Tool For Application O Diabetic Retinopathy 16

17 Diabetic Retinopathy Detection MAJOR DISEASE GOING TO BE DETECTED: 1)MICRO ANEURYSM, 2)GLAUCOMA STEPS FOR VESSEL DETECTION CANDIDATE EXTRACTION (SEGMENTATION) MICRO ANEURYSM DETECTION PERFORMANCE MEASUREMENTS Input Image sensitivity, speciicity, Preprocessing accuracy, Vessel Extraction Algorithm shape eatures [such as Area' Perimeter Eccentricity Centroid], Extracted Vessels MajorAxisLength, MinorAxisLength STEPS FOR MICRO ANEURYSM CLASSFICATION OF THE PATIENT CONDITION DETECTION Normal, Mild, Moderate, Severe Input Images (Training) STEPS FOR GLAUCOMA DETECTION Implementing 5 Algorithms For Preprocessing 1. Input Image. 2. Implementing 5 Algorithms For Candidate Preprocessing. Extraction 3. Optic Disk Segmentation. 4. Optic Cup Segmentation Area. Apply The 25 Combinations 5. Cup To Disk Ratio. Quantity Analysis (Entropy) 6. Glaucoma Detection. Best Combination Selection 7. Classiication O The Patient Condition. Input Image (Testing) Reprocessing) 8. Perormance Measurements. 17

18 Conclusion DR is extremely asymptomatic disease in the premature stages and it could lead to lasting vision loss i untreated or long time. Beore patients reaching to last stage ophthalmologist can detect the disease prissily & can avoid vision loss, using Image Processing Technique. Outcomes will become available in terms o accurately tracking or monitoring the patient speciic evolution o DR. The severity o DR the area o these eatures will be calculated. Based on the results o area computation, the system uses the classiication as normal, mild, severe to identiy the stages o non-prolierative DR and prolierative DR. 18

19 Results achieved : 1) The concept o Canny ilter detection o signals is used to detect piecewise linear segments o blood vessels in images. 2) Algorithm o Gaussian ilter is capable to detect looping structure in the blood vessel which is to maniestation o abnormal symptom. 3) MA candidate extraction 4) Median Filters can be very useul or removing noise rom images. 5) Fuzzy Gaussian Filter eature extraction o images. It is very essential to extract inected blood vassals to diagnose early thereore iltering is requently utilized in image processing 6) Smooth noise & to enhance or detect eatures within an image. To improve quality o image linear & non-linear method may be used.