Magnetic Resonance Imaging For Early Detection Of Brain TumourMade Possible Using Edge Detection And Automated segmentation

R.RAJESHREE And S.PREETHIR.M.K.College Of Engg. And Tech., ECE-IV Year

A tumour is an abnormal growth caused by cells reproducing themselves in an uncontrolled manner. The reason why brain tumours occur remains a mystery (as research into environmental and genetic factors is limited) so there is no way to predict who will get brain tumour.Earlier detection of brain metastases is critical for improved treatment. Annually brain Cancer accounts for 189000 new cases and 142000 death. Various image modalities are available for acquisition of image of detected region. The image obtained using these modalities must be processed in order to enhance its appearance and get clear idea about the kind of disease.First line screening will thus help in categorizing patients based on their difficulty level and give an economical solution to the diagnosis of tumor for weaker section. For this screening, we have first edge detected the image, identified the ROI and then verified the presence of tumor. This ROI is then used to classify the tumor type as benign or malignant. The depth of tumor cells, which is proportional to the area of ROI, is also calculated. Thus First line screening will provide a solution to the economical burden of diseased people and the social burden of medical practitioner. In our work, edge detection technique is used for detecting the tumor region in the brain image. Region of Interest (ROI) is then detected using edge analysis. Threshold based segmentation is finally done to enhance the tumor affected region in the non-contrast enhanced MRI images. Introduction:Life threatening brain tumour:A brain tumor, or tumour, is an intracranial solid neoplasm, a tumor (defined as an abnormal growth of cells) within the brain or the central spinal canal.

Any brain tumor is inherently serious and life-threatening because of its invasive and infiltrative character in the limited space of the intracranial cavity. However, brain tumors (even malignant ones) are not invariably fatal, especially lipomas which are inherently benign. Brain tumors or intracranial neoplasms can be cancerous (malignant) or non-cancerous (benign); however, the definitions of malignant or benign neoplasms differs from those commonly used in other types of cancerous or non-cancerous neoplasms in the body. Its threat level depends on the combination of factors like the type of tumor, its location, its size and its state of development. Because the brain is well protected by the skull, the early detection of a brain tumor occurs only when diagnostic tools are directed at the intracranial cavity. Usually detection occurs in advanced stages when the presence of the tumor has caused unexplained symptoms.Primary (true) brain tumors are commonly located in the posterior cranial fossa in children and in the anterior two-thirds of the cerebral hemispheres in adults, although they can affect any part of the brain.Need for smart diagnosis: The shortage of radiologists and large volume of MRI to be analyzed make such medical image readings labor intensive and cost expensive. In dealing with human life, the results of human analysis involving false negative cases must be at a very low rate. This case is less probable. Further, it has been proven that double reading of medical images could lead to better tumor detection. But the cost implied in double reading is very high, thats why smart diagnosis to assist human in medical institution is of great interest nowadays.Due to large number of patients in Intensive Care Units and the need for continuous observer of such conditions, several techniques for automated diagnostic system have been developed in recent years to attempt to solve this problem. Such techniques work by transforming the mostly qualitative diagnostic criteria into quantitative feature classification problem.Techniques used:Several technological advances have been developed which improve the quality and efficacy of brain tumor surgery. Magnetic Resonance Imaging (MRI) scanners differentiate various soft tissues, andfunctional MRI (fMRI) or Positron Emission Tomography (PET) acquire functional information modality. Theseassistant diagnostic devices are much helpful for the doctor during disease diagnosis and treatment, as well as decreasing the invasive pain of the patient.In our work, edge detection technique is used for detecting the tumor region in the brain image. Region of Interest (ROI) is then detected using edge analysis. Threshold based segmentation is finally done to enhance the tumor affected region in the non-contrast enhanced MRI images. To check the validity of proposed method, MATLAB Version-7.6.0.324 (R2008a) is used.

ALGORITHM:

READ IMAGE DATABASE

PREPROCESSING

SKULL STRIPPING

CONTOUR DETECTION

FIRST LEVEL DECISION

POSTPROCESSING

TUMOR REGION IDENTIFICATION

*AREA CALCULATION

*THRESHOLDING

SECOND LEVEL DECISION

Preprocessing of MR image:SKULL STRIPPING is a preprocessing method used to remove the unwanted non-brain tissues from the MR image. By skull removal we will get rid of the outer elliptical part in the image which avoids chance of misclassification. The steps involved in skull removal are1. Find the size of the image and store it in separate variables.2. Observe the range of gray values in the skull region.3. Perform iteration for changing all the gray values in the skull region to black.4. Repeat the steps 2 & 3 if the result is not accurate. CONTOUR DETECTION:In this method ,a new contour detection method is studied for detecting brain tumour regions based on their gradient magnitude information.Gradient magnitude data is generated from brain slice image intensity or perceived brightness information.Contour map of the brain tumor is generated by using gradient magnitude differences of the template masks and sample masks raw pixel and perceived brightness or luminance.Then this differencies are averaged and normalized to produce edges profiles of the brain tumor region contours.This data is used by the remote surgical devices for removing the tumor area.ROI EXTRACTION:The tumor region is extracted from the original image. This ROI is used for Area calculation. The ROI is first identified using the contour map and then its location in the original image is detected. This information about the location is used to extract the tumor region from the entire image. The mean value of the pixels in this region is evaluated which approximates to the area of the brain tumor.

Automated Segmentation:

An automated brain tumor segmentation method was developed and validated against manual segmentation with three-dimensional magnetic resonance images in 20 patients with meningiomas and low-grade gliomas. General segmentation framework.We adopted a general algorithm called adaptive templatemoderated classification .The technique involves the iteration of statistical classification to assign labels to tissue types and nonlinear registration to align (register) a digital anatomic atlas (presegmented anatomic map) to the patient data. Statistical classification was used to divide an image into different tissue classes on the basis of the signal intensity value. If different tissue classes have the same or overlapping grey-value distributions (eg, cerebrospinal fluid and fluid within the eyeballs), such methods fail. Therefore, additional information about the spatial location of anatomic structures was derived from a registered anatomic atlas (manually segmented MR image of a single subject). Objects of interest were identified on the classified images with local segmentation operations (mathematic morphology and region growing)

threshold algorithm can be used to extract the intracranial area from the enhanced images. Due to complex structures of different tissues such as the gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in the MR images, extraction of useful features is challenging task. Variability in tumor location, shape, size, and texture properties further complications the search for robust features. Intensity is an important feature in segmenting tumor from other tissues in the brain. This intensity information is taken as the key factor in our method. The mean pixel value of the tumor region is taken as the threshold level for segmentation. The steps involved are1. Calculate the mean of the tumor region.2. Set this mean value as threshold level (T).3. The pixels of image with intensity below T are converted to 0. 4. Those pixels with intensity above T are made 1.5. The white region thus represents the tumor in the brain image.

Matlab coding: Reading image: >> f = imread (8. jpg);

For example,>> f = imread ( D:\myimages\chestxray.jpg);reads the image from a folder called my images on the D: drive, whereas>> f = imread( . \ myimages\chestxray .jpg);reads the image from the my images subdirectory of the current of the currentworking directory. Function size gives the row and column dimensions of an image:>> size (f)ans = 1024 * 1024

Displaying images:

imshow(f,g)Where f is an image array, and g is the number of intensity levels used todisplay it. If g is omitted ,it defaults to 256 levels .>>figure ,imshow(g)Writing images:>>imwrite(f,patient10_run1,tif)

consider the following use of structure variable K to commute thecompression ratio for bubbles25.jpg:>>K=imfinfo(bubbles25.jpg);>> image_ bytes =K.Width* K.Height* K.Bit Depth /8;>> Compressed_ bytes = K.FilesSize;>> Compression_ ratio=35.162>> res=round(200*2.25/1.5);>>imwrite(f,sf.tif,compression,none,resolution,res)

code=Dec2Binfar(Tcod8,DIFLcd) function code=Dec2Binfar(Tcod8,DIFLcd)LT=length(Tcod8);Lcd=LT*8-DIFLcd;k=8;for j=1:LT for i=0:7 cd(k-i)=fix(Tcod8(j)/(2^(7-i))); if Tcod8(j)>=2^(7-i) Tcod8(j)=Tcod8(j)-2^(7-i); end; end; k=k+8;end;for i=1:Lcd code(i)=cd(i);end;

RESULTS AND DISCUSSIONS:The proposed method has been applied for different brain images and the tumor affected images are processed further to enhance the tumor region alone. The results obtained for different images are shown below: In figure1, the tumor affected image is taken and the proposed method is applied. Figure.1.A is the actual image. Figure.1.B is the output image obtained after skull removal. This image is devoid of non brain tissues that are present in the MRI. The skull stripping is employed so that miss conclusion of tumor image can be avoided. Figure.1.C represents the contour map drawn based on the intensity variation in the image. This map helps in classification of the image by making the edges more visible. Using all the information obtained, Figure.1.D is generated based upon the tumor area. This enhanced image makes the tumor more visible making it suitable for quick treatment and removal.

Figure1.A Original ImageFigure1.B After Skull Stripping Figure1.C Contour of BFigure1.D Enhanced Image The original image is also processed for better understanding of the method and the results of it areFigure2.A Original Image Figure2.B Skull Removed Figure2.C Contour mapFrom the figure2.C its clearly seen that no abnormality i.e. mass of expected type is present which confirms the absence of tumor. Thus the method produces a considerable result for detection of tumor from the brain MR images.

CONCLUSION AND FUTURE WORK:The work in this research involves classification of normal and abnormal brain image and provides first line screening which helps the patient to be directed to the concerned physician. In this paper, contour detection method is studied for detecting tumor regions in brain images which is based on the pixel intensities. Thus image enhancement is done without using contrast agent which proves very advantageous. We intend to prove, this technique will be cost effective and is more economical.Further work is required to extend the tools to a broader range of brain tumors (eg, glioblastoma multiforme). Future clinical studies on the accuracy and reproducibility of our technique in a larger population will be necessary to determine its practical use in a clinical setting. We also try to make fully automated machine vision identification which will help in getting better result than that of human analysis.REFERENCES

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