Watershed Segmentation for Tumor Detection through MRI using Thresholding

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Watershed Segmentation for Tumor Detection through MRI using Thresholding

Manorama Sharma

, G. N. Purohit

, Saurabh Mukherjee

1Research Scholar, Banasthali University, Rajasthan, India

2-3CSE Department Banasthali University, Rajasthan, India Abstract: Medical imaging plays a significant role in the field

of medical science. In present scenario image segmentation is used to extract abnormal tissues from normal tissues clearly in bio-medical images. Segmentation includes extraction of tumor from MRI images and during extraction size and location of tumor is detected. Automatic detection of tumor through brain MRI is effective and consumes lesser time which helps the doctor in diagnosis. A Tumor may lead to cancer, which is major leading cause of death and responsible for death worldwide. Automation of tumor detection is required for detecting tumor at right stage. Watershed segmentation is the commonly used technique for tumor detection. We used watershed segmentation with the help of gray scale image on MRI images followed by thresholding and morphological operator for detecting tumor. The affected part of the brain, size and shape of tumor from MRI image is identified with the help of MatLab R2013a.

Keyword: Brain Tumor, MRI, Watershed, Threshold, Morphological Operator.

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Brain is a vital organ of the body. Brain is responsible for controlling all over functioning such as memory, learning, emotions, and blood vessels. Sometime unnatural growth in the form of lump is found and this growth may be benign or malignant. Identifying tumor affected part within brain is called brain tumor detection . Abnormal cell in brain are called tumor. Unwanted cell grows in brain which may cause death of the human being. There are three stages:-

Benign: In this type tumor of normal tissues are not affected by abnormal tissues.

Pre-Malignant: If it not diagnosed properly, it can convert into cancer.

Malignant: It is cancer and may cause death.

For finding tumor size, shape, and type MRI and CT scans are commonly used. MRI is noninvasive so it is very much popular among people. MRI is useful for extracting soft tissues o and shows the internal structure of the body. MRI shows the difference between normal tissues and abnormal tissues. In this paper MRI images are used for finding affected area in brain. It is not harmful for human body because there is no radiation.

Image segmentation is used to extract the edges of affected part of human body for better diagnosis. With the help of image segmentation doctors can identify tumor

shape and size which helps in diagnosis. Image processing is used to enhance image quality to extract information from acquired image. Brain tumor is a dangerous disease commonly found in human being. The detection of tumor at an early stage reduces the changes of death of the patient.

II. LITERATURE REVIEW

Hiran and Doshi [2] developed a technique for image enhancing for brain tumor detection. Their algorithm was based on digital image segmentation. This algorithm was used to present edge pattern and segment of brain tumor through MRI images. Using this technique they were successful in finding the size and region of tumor. They used preprocessing, image enhancement, thresholding and Morphological operation. Color image was obtained and then it was converted into gray for processing.

Syed and Narayanan [3] proposed a method for Brain Tumor Detection based on artificial neural network categorized into Multi-layer perceptron neural network.

They used segmentation for feature extraction and developed a method to discriminate normal and abnormal tissues from MRI scaned images. It was helpful to doctor to analyze stage of cancer and was less time consuming.

For this pupose preprocessing, histogram, binarization, thresholding, Morphological operation, GLCM based feature extraction and BPN based classifier were used.

Viji and JayaKumari [4] developed an effective modified region growing technique. Comparative analyses were made for the normal and the modified region growing using both the Feed Forward Neural Network (FFNN) and Radial Basis Function (RBF) neural network. The results were better than normal technique. Technique was applied on MRI images for tumor detection. For evaluation of the proposed method the sensitivity, specificity and accuracy values were used.

Patil and Bhalchandra [5] proposed a system that using watershed and thresholding for image segmentation.

Segmentation was used to detect abnormal portion in medical images. In their algorithm; reads the image then covert into gray image for processing. For enhancing image high filter applied on MRI image which helps in finding tumor boundaries and reducing noise. They applied thresholding followed by watershed segmentation.

Morphological operator was implemented for finding

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exact location and size of tumor. This technique was very effective and simple for extracting tumor in MRI image.

Saini and Mohinder [6] proposed an approach for image segmentation using mathematical morphology. They found the density of cluster to detect tumor in MRI image.

OTSU’S method and optimal global thresholding was used for image segmentation. Pre processing was applied for segmentation followed by wavelet which helps doctors for the medical imaging and extract tumor. They found different type of tissues in image and separated abnormal tissues from normal tissues by their algorithm.

Oo et al. [7] used watershed segmentation and morphological operator for detecting tumor from MRI images. Their system includes filtering skull stripping, segmentation and area calculation. For segmentation preprocessing was done. Skull stripping was based on thresholding followed by marker controlled watershed segmentation. They splited the tissues in groups from normal brain image. Finally tumor region was detected with the help of morphological operation and they determined the location of the tumor on the basis of pixels value of the tumor region. Then tumor area was calculated using frustum model. Proposed method determined exact location of tumor region and extracted tumor accurately from brain MRI image.

Hoseynia et al [8] proposed a method to minimize the error in the process of image segmentation and for improving edge detection in MRI (brain tumor) images, combining fuzzy c-means algorithm with watershed algorithm. They found better results by using this method and accuracy helped them to improve images, edge detection and noise reduction in brain tumor MRI images.

The results indicated that using this combination method, provided more accuracy and helped them to improve images, edge detection. They applied fuzzy algorithm before applying watershed makers and provided high accuracy in images.

Malakooti et al [9] proposed a method which combines both Neural Network and fuzzy clustering method. Using the proposed method they classified tumor region from non tumor candidate areas. They used morphological operation for extracting candidate abnormal areas and used this technique for brain tumor MRI images. They found better result compared to existing methods. The proposed technique then increased correctness for brain tumor MRI image for diagnosis.

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There are many image segmentation techniques for detecting tumor from MRI images. Watershed is effective and simple technique for tumor extraction. MRI image is taken as input which includes range of brain, a dark black

background and signs. This image is convert into grey scale image for segmentation. Segmentation is used to provide division between regions and categories. Similar types of pixels are presented in grey scale and different pixels are presented with different value. Here images are converted into grey scale and then into binary image.

Binary image is required to reduce the complexity of data.

It provides actual shape and position of the object and discriminates between foreground and background.

Tumor segmentation is done on the basis of affected cells.

This process is carried out on the basis of different behavior of pixels in brain image. Different behavior is found according to their shape, brightness and color.

Feature extraction is done for brightness, shape and texture. The flow chart for the method is given in Figure1.

Figure 3.1 Flow Chart For Proposed Method In order to explain some parts of flow chart we consider the following :-

Watershed Segmentation: It is based on gradient and used to group pixels of higher intensity. It is used to separate different in image. It divides an image for extracting tumor. Watershed used flooding process on gray image which performed by morphological operator.

Watershed finds ridges lines in images where light pixels are high and dark pixels are low. This is useful to identify or mark foreground objects and background location

Morphological Operator

Tumor Classification Input Image (Color Image) MRI Image

Gray Scale Image Segmentation is Used

Apply Threshold Technique Watershed Technique

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Threshold segmentation: It is commonly used simplest segmentation method for medical images based on intensity and is based on a threshold value. Initially the image is converted into gray scale and then into binary image. In this technique a threshold value T is selected from binary image. Histogram is frequently used to select T value from binary image. Basically it is used to extract object from background. It provides fast processing speed, easy to manipulate and lesser space for storage. Threshold technique is global and local threshold. In global threshold a single value is selected and multiple values are selected in local threshold.

Morphological Operator: used on binary image and gray image for removal of holes in foreground and background. It is also used for noise removal from background.

Common Morphological Operations: Shrinking the foreground (“erosion”), Expanding the foreground (“dilation”),Removing holes in the foreground (“closing”),Removing stray foreground pixels in background (“opening”), Finding the outline and skeleton of the foreground

There are no individual techniques for detecting tumor.

For different body part different segmentation techniques are used. A combination of two techniques always shows a better result than other. We used threshold with morphological operator. Steps which are carried out for detection of tumor are enlisted below.

Step 1: Consider MRI scan image.

Step2: Convert image into gray image for segmentation.

Step 3: Apply sobel operator.

Step 4: Filtration is applied for enhancement.

Step 5: Watershed is applied to extract different pixels from background.

Step 6: Apply threshold technique on segmented image.

Step 7: Apply morphological operator imdilate for extraction.

Steps8: Finally bwboundaries is implemented for boundaries of tumor.

The following terms are use in this system:-

 Raw image is input for process then gray and histogram equalization is applied as follows:-

MATLAB code:

gray = rgb2gray(I); I = histeq(gray);

 Sobel operator is used for finding edges .It is used to show the edges of maximum gradient. The gradient of

the image is calculated for each pixel position in the image.

 Then filtration is applied for image smoothing.

 Watershed is implemented for foreground markers which used to show connected pixels and separating background. In this paper

 Thresholding is applied for selecting threshold point.

Hard thresholding is applied for value selection.

Mathematically representation is as follows:- g(m, n)= 0 for(m, n) < t……… [12]

255

Here f(m, n) represent the input value g(m, n) represents the output image t represents the threshold parameter

 Regionprops method is used to measures a set of properties which return value STATS is a structure array of length max(L(:). MATLAB code:

stats=regionprops(label,'Solidity','Area');

Dilate is used to dilate image by using this function with the help of structure element. This is used to convert pixel into background pixels which is near to background pixels. After applying this objects become smaller. Mathematically representation is as follows:- (AƟB)(x)={x∈ X, x=a+b: a ∈A b∈B}

A-Matrix of binary image B- Mask MATLAB

MATLABcode: tumor=imdilate(tumor,se); Where SE is structuring element

 Finally boundaries are created for connected pixels which tumor in brain MRI image.

MATLAB code:

B=bwboundaries(tumor,'noholes');

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Figure 3.2 Sample Images of Data Set a) b) c) d) V.

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Next figures show the images as an output. i.e grayscale image, filtered image ,watershed gradient image, Watershed magnitude, watershed segmented image, threshold image, Finally extracted tumor from MRI image and image with tumor. For this system 20 images are taken for analysis. As tumor in MRI image have an intensity more than that of its background so it become very easy locate it and extract it from a MRI image.

Results are as follows:-

Figure 3.3 Gray Image, Watershed Gradient, Gradient Magnitude, Watershed Image

Figure3. 4 Tumor Part, Tumor With Image

Figure3. 5 Gray Image, Watershed Gradient, Gradient Magnitude, Watershed Image

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Figure3. 6 Tumor Part, Tumor With Image

Figure3. 7 Gray Image, Watershed Gradient, Gradient Magnitude, Watershed Image

Figure3. 8 Tumor Part, Tumor With Image VI.

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Segmentation techniques are used to detect brain tumor. Ii is very helpful in patient treatment. Brain tumor detection and classification is successfully implemented by using the image processing tool box in MATLAB R2013a. We present an automated recognition system for the MRI image using the watershed, filtration, threshold and morphological operator. It is observed that results are improved than the individual technique. In image processing thresholding is very important for segmentation. By this objects are extracted from background. It provides fast processing, smaller storage space, and easy to manipulate. The considerable iteration time and the accuracy level are improved as compared to individual technique.

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