An Automatic Segmentation & Detection of Blood Vessels and Optic Disc in Retinal Images

International Conference on Communication and Signal Processing, April 6-8, 2016, India

An Automatic Segmentation & Detection of Blood Vessels and Optic Disc in Retinal Images Anchal Sharma, Shaveta Rani

Abstract—Conceptual Segmentation is a critical technique in medical imaging. The Processes of identification and division of optic circle and veins are the fundamental strides for the analysis of a few infections that causes visual deficiency like diabetic retinopathy, hypertension, glaucoma and different visual deficiency ailment. These diseases lead to loss of vision. The vein data is required or used to ascertain a roughly position of the optic circle. In this paper, preprocessing using neutrosophy presented as the first step in proposed method that used for extraction of blood, which gets better results. Analysis of retinal images is an important aspect for the diagnosis of eye diseases.

[1]Glaucoma is a chronic, and irreversible disease that is connected with the brain nerve is progressively damaged so, patients suffer from loss of visual power and blindness.This paper is presenting the techniques for segmentation of blood vessels and detection of optic disc. The Neutrosophy strategy is utilized i.e. Neutrosophy,

Keywords—Diabetic retinopathy, Hypertension, Glaucoma, Retinal Images, Ailment, Neutrosophy

I. INTRODUCTION The division is the procedure of isolating the picture into different parts[13]. The Processes of identification and division of optic nerves and veins in retinal pictures are mind boggling and essential step[23]. The aim of segmentation is to just simplify the representation of a digital image and easier to understand. Here[14],The Segmentation of blood vessels and optic disc is performed for the detection or diagnosis of eye diseases .There are some techniques that focus on segmenting blood vessels[3], but very few are related to retinal image blood vessels features like diameter,color, tortuosity are used by ophthalmologists, then that processes take long time and prone to human error[11]. DR(Diabetic Retinopathy) is a diabetic stage that leads to vision loss and occurs due to the progression of diabetes[15]. The optic nerve is found 3mm to nasal in the area of the human eye and a blind spot i.e. a spot on the retina’s surface. The beginning portion of the optic nerve in the retina is called Optic Disc[15]. Glaucoma is the second main cause of blindness[1], it is a reason behind the blindness for approximately 5.2 million cases.

Anchal Sharma (M.Tech Student) Department of Computer Sci & Engg, GZS College Campus of Engg.& Tech, Bathinda anchalsharma1992@gmail.com Shaveta Rani (Associate professor) Department of Computer Sci & Engg, GZS College Campus of Engg& Tech, Bathinda garg_shavy@yahoo.com

Fig. 1. Normal retinal image(A retinal image in which blood vessels and optic disc are detected ) studies the root, nature, the extent of neutralities and their collaborations with various ideational spectra. It is an another logic to extend the fuzzy logic and is the premise of neutrosophic logic, neutrosophic set, and neutrosophic statistic[2]. Image division is a step for image description, design acknowledgment, PC vision. Many current systems for image characterization and acknowledgment exceedingly rely on upon the division results. In this paper, Neutrosophy is connected to image processing by characterizing a neutrosophic space, which is specified by three subsets T, I and F [2]. Then, Filtering is performed to remove the unwanted components from the image. Here, the Average Filter and Wiener Filter are used i.e. Average Filter is useful for graining noise from a photograph; each pixel is set according to the mean of the pixel in its neighborhood. In [18]Similarly ,Wiener Filter is used to minimize the difference between input and output images, but the Wiener channel requires to knowing the force spectral thickness of real image which is unavailable in practice[18].Adaptive Histogram adjustment works on little information districts instead of the whole picture and additionally differentiate upgrade & contrast enhancement can be constrained keeping in mind the end goal to abstain from amplifying the commotion which may be displayed in the image[8]. So, Adaptive Histogram

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Equalization system works altogether superior to any other regular Histrogram Equalization for most pictures.

The pixel P(i,j)={T(i,j),I(i,j),F(i,j)}, which are defined as following

The rest of the paper is organized as follows: Section II illustrate proposed algorithm. Section III gives proposed methodology. Finally conclusions and features scope are given in Section IV and V respectively.

T(i,j)= ̅( , ) =

(, )

∗

∑

( , )

II. PROPOSED ALGORITHM Step 1- Read the Image from file.

I(i,j)=

Step 2- Apply Neutrosophic technique on the elements of an image to get the binary image. Neutrosophy is applied to image processing by defining a neutrosophic range in space which is described by T,I&F sets.

Step 3-Adaptive Histogram Equalization is performed for enhancement of an image i.e. enhance the contrast of gray scale image I by transforming the different values.

( , )=

(, )

( ( , ) − ̅ ( , ))

F(i,j)=1-T(i,j) ̅ (i, j) is the local mean value of the pixels. And another is defined here as; (i, j) is the absolute value of the difference between intensity g(i, j) and ̅ (i, j).

Contrast enhancement

Step 4- Apply Morphology for segmentation.

Step 5-Average Filter (useful for removing grain noise from a photograph, each pixel get sets to the average of the pixels in its neighbourhood).

Step 6-Wiener Filter(filters an information sequence utilizing an advanced channel which works for both genuine and complex I/Ps ).

Improvement of complexity and intensity in grayscale image is performed with the Adaptive Histogram Equalization with utilizing CLAHE Contrast Limited Adaptive Histogram Equalization[8] i.e. performed on small sections of an image as compared to entire image. This is developed & tested on image of standard DRIVE dataset. It provides a total description of appearance of an image.

Step 7-Results .

III. PROPOSED METHODOLOGY In this paper, a new approach is presented for the segmentation of blood vessels. In [7] Preprocessing is a procedure used to perform enhancement with Adaptive Histogram Equalization, Morphology and then removes all small pixels components from an image after that different filters are used for noise removal. This working method is in detail as given below:A. Preprocessing Using Neutrosophy and Enhancement with Adaptive Histogram Equalization In[2], Mohan J. considers Neutrosophy , it is a branch of philosophy that studies the neutralities and their cooperations with different ideational spectra. Every event has a certain degree of Truth, False and an Inter mediate to be considered. An event is considered with {S}, its opposite {Anti- S} and neutrality {Neut- S}.NS provides a powerful technique to deal with intermediate values or middle value. A neutrosophic image is represented with three different membership functions sets T,I&F. P is characterized by three membership sets T, I, F. A pixel P in the image is described as P (T,I,F).

(a)

(b)

Fig. 2. Preprocessing of retinal image (a)Binary image(b)Contrast enhanced image

A. Segmentation with Morphology A Morphologically based approach in[9] is used for detection and segmentation of blood vessels, this works to remove the small objects from the binary image, every single associated part that have less than p pixels, producing another binary image[9]. Morphological Open and Close operations are performed by using Dilation and Erosion respectively. The morphologically reconstructed opened image, and closed images are absolutely removed to detect the blood vessels from retina fundus [17] and to complement the resultant

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image. After that, filteration processes are performed with different filters.

(a)

an advanced channel which works for both genuine and complex inputs. The channel is an immediate structure II

(b)

(a)

Fig. 3(a). Original image extracting blood vessels (b) Morphological reconstruction image

(b)

Fig. 5(a). Image with noise (b) Result of Average Filter

C. Filtering techniques Due to noise problem, it is difficult to detect the diseases. To eliminate the noise, the filtering techniques with some filters such as Average Filter and Wiener Filter can be used.

Average Filter The idea of mean filtering[18] is basically to supplant every pixel esteem in a picture with the mean (`average') approximation of its neighbors, including itself. This filter has the impact of killing pixel values which are unrepresentative of their environment. Mean sifting is normally considered as a convolution channel. Like different convolutions, it is based around a bit that speaks to the shape and size of the area to be inspected while figuring the mean. Frequently a 3Ă—3 square portion is utilized, as appeared in given Figure 4, albeit bigger parts (e.g. 5Ă—5 squares) can be utilized for more serious smoothing. (Note that a little part can be connected more than once, keeping in mind the end goal to create a comparative however, not indistinguishable impact as a solitary go with a substantial piece).

1 9

1 9

1 9

1 9

1 9

1 9

1 9

1 9

1 9

(Ex) Fig. 4. 3Ă—3 averaging kernel often used in mean filtering Wiener Filter is a 2-D versatile commotion evacuation separating filter. This channel capacity channels an information succession utilizing

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(a)

(b)

(c)

(d)

(e) Fig. 8. Result Images (a)Colored Retinal Image (b)Original Extracted Image(c)Morphological Reconstruction Image(d)Average Filtered Image (e)Wiener Filtered Image

transposed execution of the standard diverse mathematical statement, which is inaccessible practically speaking.

In[18]The Wiener channel is utilized to deliver an appraisal of a fancied or target arbitrary procedure by direct time-invariant (LTI) separating of a watched loud process, accepting known stationary flag ,commotion spectra. The Wiener channel minimizes the mean square mistake between the assessed arbitrary procedure and the wanted process. The result of the Wiener Filter is shown in Fig. 6(b).

IV. RESULT ANALYSIS This gives the analysis of resultant images of retinal input image that areD. Flowchart

IV. CONCLUSION The proposed approach for automatic segmentation of blood vessels is an effective method for medical diagnosis. The difficulty in the diagnosis was clear segmentation of patterns in the retinal image. The approach for blood vessel detection and segmentation in retinal images is presented by integrating Neutrosophic techniques, Morphological reconstruction scheme, also involves Adaptive Histogram Equalization for enhancing contrast, and Average Filter &Wiener Filter are used for filtering & noise reduction. Due to some errors found in blood vessel segmentation in retinal image, this methodology helps in correcting vessels information for improving measurements in result analysis. This is also helpful and may be utilised for recognition of diseases like Diabetic Retinopathy, Glaucoma, Hyper tension.

V. FUTURE SCOPE

Start

Input retinal image

Apply neutrosophy

Future scope of this project is to detect eye diseases from affected images thus making mankind beneficial to a large extent, so that by diagnosing diseases prevent from loss of vision power or blindness. From the result images, it is identified the concept of the whole work in the system. We are further working on improving the processing speed, efficiency, accuracy and performance of the system and include optimal methods or combining of some techniques for classification or separation of an optical disc and blood vessels for better ailment of eye diseases.

Contrast Enhancement REFERENCES [1]

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Results Fig. 7. Flowchart of proposed system

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