6 ijaers jan 2016 35 analysis of medical image processing and its application in healthcare

International Journal of Advanced Engineering Research and Science (IJAERS)

Vol-3, Issue-2 , Feb- 2016] ISSN: 2349-6495

Analysis of Medical Image Processing and its Application in Healthcare Dr. K.Sakthivel, B.R.Swathi, S.Vishnu Priyan, C.Yokesh Department of CSE, K.S.Rangasamy College of Technology,Tiruchengode Abstract—Medical image processing is the most challenging and emerging field now a days.With fundamentally improving technical knowledge, enhanced technological support and well-constructed medical equipment, medical diagnosis is increasingly becoming easy for doctors and medical staffs. However accurate diagnosis is still not possible. The approximate values and prediction may effect to a certain range but do not provide a cure.This is due to using of multiple and multiple testing systems when choosing between best and reliable becomes questionable.When it comes to Scan,X-rays and MRIs, the image results between each test samples shows significant variations and it is still arguable to find out the best pick.As MRIs are better choice due to it’s considerable efficiency rate, it has been often preferred in medical image diagnosis.Processing of MRI image is one of the integral part of this field. The proposed strategy is to detect,analyze and extract the tumor from patient’s MRI scan images of the brain. This method incorporates with some noise removal functions, segmentation,filtering processes and morphological operations which are the basic concepts of image processing.MATLAB provides a complete full packed environment to support image analysis domain with some built-in function and wide range of image processing tools.Thus,detection and extraction of tumor cells from MRI scan images of the brain is done by using MATLAB software.

Keywords— GUI, MATLAB, MRI, Segmentation, Enhancement.

I. INTRODUCTION Image processing is a method to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. It is a type of signal dispensation in which input is image, like video frame or photograph and output may be image or characteristics associated with that image. Usually Image Processing system includes treating images as two dimensional signals while applying already set signal processing methods to them. It is among rapidly www.ijaers.com

growing technologies today, with its applications in various aspects of a business. Image Processing forms core research area within engineering and computer science disciplines too.

The two types of methods used for Image Processing are Analog and Digital Image Processing. Analog or visual techniques of image processing can be used for the hard copies like printouts and photographs. Image analysts use various fundamentals of interpretation while using these visual techniques. The image processing is not just confined to area that has to be studied but on knowledge of analyst.

Association is another important tool in image processing through visual techniques. So analysts apply a combination of personal knowledge and collateral data to image processing.

Digital Processing techniques help in manipulation of the digital images by using computers. As raw data from imaging sensors from satellite platform contains deficiencies. To get over such flaws and to get originality of information, it has to undergo various phases of processing.

The three general phases that all types of data have to undergo while using digital technique are Pre- processing, enhancement and display, information extraction.

Image processing basically includes the following three steps.

•

Importing the image with optical scanner or by digital photography

•

Analyzing and manipulating the image which includes data compression and image enhancement and spotting patterns that are not to human eyes like satellite photographs

•

Output is the last stage in which result can be altered image or report that is based on image analysis.

II. LITERATURE REVIEW Image processing is the field in which the information from images can be retrieved using suitable algorithm. The morphological image processing is used to detect the tumors from the brain either malignant or non-malignant tumors. The brain tumors some times change to malignant Page | 25

International Journal of Advanced Engineering Research and Science (IJAERS) will leads to cancer. There are several techniques to capture image of brain like MRI, CT scan etc… A tumor is a mass of tissue that grows out of control of the normal forces that regulates growth. The multifaceted brain tumors can be split into two common categories depending on the tumors beginning, their enlargement prototype and malignancy.

Primary brain tumors are tumors that take place commencing cells in the brain or commencing the wrapper of the brain.

U.Vanitha et al performed morphological operations like dilation, erosion etc… was done to remove the tumor from the MRI Image. Recent techniques achieved in researches for detection of brain tumor can be broadly classified as 1. Histogram based method.

2. Morphological operation is applied to MRI images of Brain.

3. Edge base segmentation and color base segmentation.

4. Cohesion self-merging based partition K-mean [2].

The proposed work carried out processing of MRI brain images for detection and classification of tumor and nontumor image by using classifier. The image processing techniques like histogram equalization, image enhancement, image segmentation and then extracting the features for Detection of tumor. Extracted feature are stored in the knowledge base. An appropriate classifier is developed to recognize the brain tumors by selecting various Features.

The system is designed to be user friendly by using MATLAB GUI tool.

Dr. P.V. Ramaraju et al proposed pre-processing of MRI images is the primary step in image analysis which perform image enhancement and noise reduction techniques which are used to enhance the image quality, then some morphological operations are applied to detect the tumor in the image. The MRI brain image is acquired from patient’s database and then Image acquisition, pre-processing, image segmentation is performed for brain tumor detection [11].

SivaSankari.S et al used median filter for removing noise from an image. The median filter is a non-linear digital filtering technique, is often used to remove noise. Median filtering is very widely used in digital image processing because, under certain conditions, it preserves edges while removing noise. The median filter is normally used to reduce noise in an image, somewhat like the, mean filter.

However, it often does a better job than the mean filter [13].

III. PROPOSED SYSTEM Segmentation is a process that is used to identify an object orpattern in the given work space. The main goalis to partition an image into several segments so that each www.ijaers.com

Vol-3, Issue-2 , Feb- 2016] ISSN: 2349-6495

segment can be analyzed precisely.The preliminary function is to read the input image .Here the input image is MRI image. The input image may contain RBG color and this RBG color should be removed so that the further process will be enhanced clearly. So the RBG color should be converted to greyscale image. Segmentation operation is performed with the resulted greyscale image. There may be presence of noise in the image. So it must be removed by the noise removal technique. Then the morphological operation includes detection of the entire cell, dilation of the cell, filling the entire gaps, smoothing of the object is done simultaneously.The steps are as follows:• Segmentation • Noise Removal • Morphological Operation • Image Enhancement • Image Filtering The preliminary function is to read the input image.Here the input image is MRI image. The input image may contain RBG color and this RBG color should be removed so that the further process will be enhanced clearly. So the RBG color should be converted to greyscale image. Segmentation operation is performed with the resulted greyscale image.

There may be presence of noise in the image. So it must be removed by the noise removal technique. Then the morphological operation includes detection of the entire cell, dilation of the cell, filling the entire gaps, smoothing of the object is done simultaneously.

3.1 Segmentation Image segmentation is a process where the image can be partitioned into cluster of pixels which are similar based on some criteria. Different groups must not interact with each other, and neighboring cells can be compared. The result of segmentation is the splitting up of the image into connected areas. Thus segmentation is concerned with dividing an image into meaningful regions.

MR image segmentation is an important but a difficult problem in medical image processing. In general, it cannot be solved using straightforward, conventional image processing techniques. There will be some variation in signal intensities for one same tissue type, which affect the tissue intensities.Segmentation process is thus used to partition such cells. By using MATLAB, the tumor is detected as a result of segmentation and optimal global thresholding. The brain tumor detection is a great help for the physicians and a boon for the medical imaging and industries working on the production of CT scan and MRI imaging.

Page | 26

International Journal of Advanced Engineering Research and Science (IJAERS)

Fig. 1: Segmentation 3.2 Noise Removal It is not sure that the Digital image contains no noise noise. Errors in the image will be in the form of that result in pixel values that do not reflect the true intensities of the real scene. There are several ways that noise can be introduced into an image, depending on how the image is created. For example:

•

If the image is scanned from a photograph made on film, the film grain is a source of noise. Noise can also be the result of damage to the film, or be introduced by the scanner itself.

•

If the image is acquired directly in a digital format, the mechanism for gathering the data (such as a CCD detector) can introduce noise.

•

Electronic transmission of image data can introduce noise.

To simulate the effects of some of the problems listed above, the toolbox provides theimnoisefunction, function, w which can be used to perform some manipulation operation with noise. noise 3.4 Morphological Operation Morphological operations results in the same sized output as the input image size.. In a morphological operation, the value of each pixel in the output image is based on a comparison of the corresponding pixel in the input image with its neighbors. By choosing the size and an shape of the neighborhood, we can construct a morphological operation that is sensitive to specific shapes in the input image.

One of the basic morphological operations rations is dilation. dilation Dilation adds pixels to the boundaries of objects in an image. The number of pixels added or removed from the objects in an image depends on the size and shape of the structuring element used to process the image. imag In the morphological dilation, the state of any given pixel in the output image is determined.

3.3 Image Enhancement To improve the interpretability or perception of information, images are enhanced and it provides `better' input for other automated image processing techniques. The principal objective of image enhancement is to modify attributes of an image to make it more suitable for a given

www.ijaers.com

Vol-3, Issue-2 , Feb- 2016] ISSN: 2349-6495

task and a specific observer. During this process, one or more attributes of the image are modified. Filtering is technique for enhancing the image.

Image division ivision is done on the basis of similar attributes and they are separated into groups. Basic purpose of segmentation is the extraction of affected regions from the image, from om which information can easily be perceived.

Thresholding is used for segmentation as it is most suitable for the present application in order to obtain a binarized image with gray level 1 representing the tumor and gray level 0 representing the background. backgroun After converting the image in the binary format, morphological operations are applied on the converted binary image. The purpose of the morphological operators is to separate the tumor part of the image. Now only the tumor portion of the image is visible, visi shown as white color.

This portion has the highest intensity than other regions of the image.

3.5Image Filtering Filter process removes unwanted component or feature in an image. It defines the feature of filters as a complete or a partial suppression on of some aspect of the image. image As a result, background noise will be removed. removed Filters do not exclusively act in thefrequency frequency domain,especially domai in the field of image processing many other targets for filtering exist. Correlations can be removed for certain frequency fre components and not for others without having to act in the frequency domain.

The most common methods used for filtering filteri of medical images till now are the low pass filtering(for filtering sharpening) and median filtering (for smoothening). But these methods have certain drawbacks like blurring the details as well as edges in an MRI image. So, high pass filtering is carried out. It sharpens the image and preserves the high frequency information within an image. Here median pass filtering is applied.

Fig. 2: Salt and Pepper P Noise Image Page | 27

International Journal of Advanced Engineering Research and Science (IJAERS)

Vol-3, Issue-2 , Feb- 2016] ISSN: 2349-6495

and robust when compared with the other classifiers.The classifiers. resulted process would certainly enhance and help the current diagnosis techniques. techniques

Fig. 3: Median Filtered Image 3.6 Image Preprocessing When MRI images are viewed on computer screen, they look like black and white but in actual they contain some primary colors (RGB) content. So, for further processing of MRI brain image, it must be converted to perfect grayscale image in which the red, green en and blue components all have equal intensity in RGB space. The original MRI brain image has properties 320x320x3 and conversion to grayscale image makes the properties 320x320.

This step is carried out to improve the quality of the image to make it ready dy for further processing. This improved and enhanced image will help in detecting edges and improving the quality of the overall image.

3.7 Image Preprocessing using MATLAB The Image Processing Toolbox is a collection of functions that extend the capabilities of the MATLAB’s numeric computing environment. The toolbox supports a wide range of image processing operations, including:

• • • • • •

Geometric operations Neighborhood and block operations Linear filtering and filter design Transforms Image analysis and enhancement ement Binary image operations

IV. CONCLUSION The main objective of the proposed methodis method to analyze the abnormal cells based on the optimal features set. This classification is performed based on obtained Magnetic Resonance Images. The accurate results depends on the selection, classification and processing techniques and datasets obtained from MRI images. Series of observations o are made on obtained datasets using MATLAB functions and the results obtainedare evaluated to be more accurate www.ijaers.com

REFERENCE [1] Mohinder Singh, Pankaj Kr. Saini (2015) “Brain Tumor Detection in Medical Imaging using MATLAB”, International Research Journal of Engineering and Technology (IRJET)Vol.02, No.2, pp.191-196 N.PonNageswaran, R.Sathappan, [2] P.Prabhu Deepak,N.PonNageswaran, U.Vanitha, (2015) “Tumor Detection In Brain Usin Using Morphological Image Processing”, Journal of Applied Science and Engineering Methodologies Vol.01, No.1, pp. 131-136 [3] V.Kala, Dr.K.Kavitha (2015)“Brain Tumor Extraction from MRI Images Using MATLAB”, International Journal of Advanced Technology in Engineering Engine and Science, Vol.03, No.1,pp.453459 [4] Mohinder Singh,, Pankaj Kr. Saini (2015) “Brain Tumor and Clustering Techniques Review”, International Journal of Emerging Technology and Innovative Engineering Vol.01, No.6, pp.110 [5] Azhar,Ed-EdilyMohd.

EdilyMohd. Muhd. MudzakkirMohd.

Mudza Hatta, ZawZawHtikeandShoon Lei Win (2014), “Brain Tumor Detection And Localization In Magnetic Resonance Imaging”, International Journal of Information Technology Convergence and Services (IJITCS), Vol.4, No.1, pp. 1-11 1 [6] M.Manikandan, Rohini Paul Pau Joseph, C. Senthil Singh, (2014) “Brain Tumor MRI Image Segmentation and Detection in Image Processing”, IJRET: International Journal of Research in Engineering and Technology Vol.3, No.1, pp.1-5 [7] Nidhi, PoonamKumari (2014), ”Brain Tumor and Edema Detection ion using MATLAB 7.6.0.324”, International Journal of Computer Engineering and Technology, Vol.5, No.3, pp. 122-131 122 [8] Rajesh C. Patil, Dr. A. S. Bhalchandra (2014) “Brain Tumor Extraction from MRI Images Using MATLAB”, International Journal of Emerging Technology and Innovative Engineering Vol.02, No.1, pp.1-4 pp.1 [8] Balakumar.B, Gayathri Devi .S MuthukumarSubramanyam, P.Raviraj, (2014) “An Automatic Brain Tumor Detection and Segmentation Scheme for Clinical Brain Images”,International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) Vol.02, No.1, pp.37-42 Page | 28

International Journal of Advanced Engineering Research and Science (IJAERS) [9]

[10]

[11]

[12]

[13]

[14]

[15]

Vol-3, Issue-2 , Feb- 2016] ISSN: 2349-6495

Arun Bansal, Geetika Gupta, Rupinder Kaur, Munish Bansal (2014) “Analysis and Comparison of Brain Tumor Detection and Extraction Techniques from MRI Images”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol.03, No.11,pp.

13274-13282 Gurpreet Kaur, Er. Karamjeet Singh (2014) “A Comprehensive Review of Various Medical Image Processing Techniques for MRI Images”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.04, No.5, pp. 10691072 Dr. P.V. Ramaraju, ShaikBaji (2014) “Brain Tumour classification, Detection and Segmentation Using Digital Image Processing and Probabilistic Neural Network Techniques”, International Journal of Emerging Trends in Electrical and Electronics, Vol.10, No.10, pp. 15-20 Sumitharaj.R, Shanthi.K (2014), “Segmentation of Brain Tumor from MRI Image by Improved Fuzzy System”, International Journal of Advances in Engineering & Technology, Vol.7, No.3, pp. 967-973 Sangeetha.R, ShenbagaRajan.A, Sindhu.M,, SivaSankari.S, (2014) “Feature Extraction of Brain Tumor Using MRI”, International Journal of Innovative Research in Science, Engineering and Technology, Vol.03, No.3, pp. 10281-10286 PrachiGadpayleand, Prof.P.S.Mahajani (2013) “Detection and Classification of Brain Tumor inMRI Images”, International Journal of Emerging Trends in Electrical and Electronics, Vol.05, No.1, pp.45-49 AruMehrotra,KimmiVerma, Shardendu Singh, VijayetaPandey (2013) “Image Processing Techniques For The Enhancement of Brain Tumor Patterns”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation EngineeringVol.02, No.4,pp.1611-1615

www.ijaers.com

Page | 29