Brain Thmor Detection using Segmentation based Object Labeling Algorithm

Amitava Halder, Chandan Giri Department of IT

Bengal Engg. and Sc. University Shibpur

Howrah-71l103, India.

Amiya Halder Department of CSE

St. Thomas College of Engg. and Tech.

amitava. halder2008@ gmail. com, chandangiri@ g mail. com

Kidderpore Kolkata 700023, India.

amiya.halder77@gmail.com

Abstract-In this paper, we propose an efficient brain tumor detection method, which can detect tumor and locate it in the brain MRI images. This method extracts the tumor by using K-means algorithm followed by Object labeling algorithm. Also, some preprocessing steps (median filtering and morphological operation) are used for tumor detection purpose. It is observed that the experimental results of the proposed method gives better result in comparison to other techniques.

Keywords: Morphological opening, K-means algorithm, Object Labeling Algorithm, Image segmentation.

I. INTRODUCTION

Image segmentation holds an important position in the area of medical image processing [1]. Segmentation can be used to detect tumor from MRI image. Throughout the few years, different segmentation methods have been used for tumor detection but it is time consuming process and also gives inaccurate result. So, computer aided system can be designed for accurate brain tumor detection from MRI images. Brain tumor can be broadly classified as primary brain tumor(the tumor originates in the brain) and secondary brain tumor (spread to brain from somewhere else in the body through metastasis) [2]. Primary brain tumors do not spread to other body parts and can be malignant or benign and secondary brain tumors are always malignant. Malignant tumor is more dangerous and life threatening than benign tumor. The detection of malignant tumor is more difficult than benign tumor. For the accurate detection of the malignant tumor that needs a 3-D representation of brain and 3-D analyzer tool. Different brain tumor detection algorithms have been developed in the past few years. M. Masroor Ahmed and Dzulkifti Bin Mohammad [1] proposed brain tumor detection method using Kmeans algorithm. It is observed that tumor is detected along with non tumor region. Also, Greg Hamerly and Charles Elkan have presented an algorithm using Kmeans clustering [3]. This algorithm can be used for

better result in image segmentation. J. Selvakumar et al. [2] has been developed brain tumor detection with K-means clustering algorithm and finally they have used FCM for better result. An adaptive K-means algorithm is considered in [4] to detect micro calcifications in digital mammograms for breast cancer detection. FCM technique is used in [5] to extract WM, GM and CSF from MRI image. M. Shasidhar, V. Sudheer Raja, B. Vijay Kumar [6] have modified FCM for fast convergence of the algorithm. In [7] a technique called Cohesion Based Self Merging (CSM) is used to refine the detected tumor area.

In addition, algorithm using threshold technique based segmentation have also been investigated in tumor detection problem in [8]. Kiran Thapaliya and GooRak K won [9] has detected tumor from MRI image using techniques based on morphological operations. In [10] template matching approach had cited to detect the tumor. A. Elamy, M.Hu [11] has predicted the growth of the tumor using similarity measure approach by combining Bayes Classifier. In this paper, we develop a brain tumor detection technique for T2-weighted MRI images, T2-weighted MR images brain tumor appears as hyper-intense with respect to normal brain tissue. The existing brain tumor detection methods are based on different unsupervised learning algorithms (K-means, Fuzzy C-means (FCM) etc). K-means is partitioned clustering approach and in FCM, membership value of each pixel is calculated so that a particular pixel can belong to a cluster center. Threshold is a particular intensity value which satisfies a predefined intensity value, it is used to separate object or Region of Interest (ROI) from the image background, chosen in the range of 0 to 255. But it is observed that clustering methods followed by threshold cannot detect tumor properly from MRI image, because the image consist of several non brain tumor tissue. For this reason we formulate the proposed method using K-means algorithm followed

by Object labeling algorithm also, some preprocessing steps (median filtering and morphological operation) is used for tumor detection purpose.

The paper is organized as follows: The new algorithn is proposed in Section II. Experimental results are presented in Section III and Section IV concludeds the paper.

II. PROPOSED METHOD

The basic purpose of this paper is to show only the tumor region. In this paper, different types of T2-weighted images are used for tumor detection. The complete procedure for the proposed algorithm is given below.

A. Pre-processing

This is the first step of the proposed method. In this step, some noises and skull remove from the images using median filter and morphological operations.

I) Noise Removal: Median filter acts as noise removal non linear tool. In this filtering technique each image pixel is replaced by the neighborhood median pixel. Median filter is robust and widely used in image processing because it preserves edges while removing noise. Another advantage is that it does not create any new intensity image pixel because median is an existing pixel value in the neighborhood window (3 x 3).

2) Morphological Opening: Morphological opening is another important preprocessing (skull removing) step. Two gray scale morphological operations, Erosion and Dilation is used for this purpose. Here, 3 x 3 square Structuring Element (SE) is considered for tumor detection.

B. Segmentation

After preprocessing step, MRI Image is segmented by K-means clustering algorithm. It is fairly simple when compared with frequently used fuzzy clustering methods [1]. K cluster centers are randomly chosen from image data. Then distance is computed for each pixel with the K cluster centers. The pixel belongs to that cluster center which gives minimum distance. Then the K cluster centers are recomputed and this process is repeated until the centers converge.

C. Construct Binary Image

Then, binary image is constructed by Threshold approach. Threshold intensity is defined as the pixel intensity value which satisfies a predefined value. After applying K-means, threshold intensity is applied over segmented image. In gray scale image 255 is considered as foreground or object pixel and 0 is considered as background pixel.

(a) (b) (c) (d) (e) (0 Figure 1. Tumor detection using proposed method: (a)Original lmage(b)applying median filter (c)appJying morphological opening (d)applying K-means method (e) applying threshold (f) detected tumor.

D. Object Labeling Algorithm

In this paper, binarized image is labeled using Object Labeling algorithm. The purpose of this algorithm is to label different objects within the image [12].

E. Detect Tumor

In the final step, detect the tumor region from labeled MRI image. The tumor occupies maximum area in the labeled image, so there is exactly one label in the labeled image whose frequency value will be the maximum. The steps of this approach are given below:

Step I : Let there are n labels {lo,h,l2"l(n-1)} in the labeled image. Count frequency (occurrence) of each label from the labeled image. Let the frequencies are {Jo,iI,12"f(n-l)} for labels {lo,h,12"I(n-1)} respectively.

Step 2: Find max {Jo , iI, 12" f(n-1)}' Let this frequency is fmax.

Step 3: Search for the label I b 0 ::; k ::; (n - 1) from the set {1o, h, 12" l(n-1)} which have the frequency value fmax.

Step 4: Perform 8-adjacency with respect to lk in the labeled image.

Step 5: Construct binary image from the labeled image.

III. EXPERIMENTAL RESULTS

The proposed method is applied on different types of tumor affected T2 weighted MRI images (20 images). The shape and size of the tumor is different and varying from image to image. The results (tumor detection) of the different MRI images using K-means followed by threshold method, FCM-Kmeans [2] and proposed method with ground truth (manually created) are shown in Figure 2. Overall error is calculated from false alarm and miss alarm. Finally, accuracy of the proposed system is measured with the help of overall error. It is observed from the result that percentage of accuracy of the proposed method is better than the other two methods (shown in Figure 3, Figure 4, Figure 5 and Figure 6). False alarm is defined as if ground truth

(a) (b) (c) (d) (e)

Figure 2. Output of the different images:(a)Original lmage(b) Ground Truth image (c) K-means followed by threshold (d) Method [2] (e) Proposed Method.

image consist unchanged pixel in the binary tumor image but proposed binary tumor image consist changed pixel. Similarly, Miss Alarm is defined as if ground truth image consist changed pixel in the binary tumor image but proposed binary tumor image consist unchanged pixel and

Overall Error = FalseAlarm + MissAlarm (1)

Al - Overall Error

ccuracy = x 100 (2) I mageDi mension

IV. CONCLUSION

In this paper, brain tumor is detected from MRI images using K-means followed by Object Labeling algorithm. When the tumor is very close to bone, Kmeans and Fuzzy c-means segmentation cannot segment

Figure 3. False alarm obtained using K-means followed by threshold. Method [2] and Proposed Method.

Figure 4. Miss alarm obtained using K-means followed by threshold. Method [2] and Proposed Method.

5000

:

Figure 5. Overall error obtained using K-means followed by threshold, Method [2] and Proposed Method.

Figure 6. Accuracy measured using K-means followed by threshold, Method [2] and Proposed Method.

the MRI image efficiently. So, new concept and different existing techniques can be used to improve the correctness of these algorithms. New MRI techniques such as Diffusion-Weighted MRI, Perfusion-Weighted MRI, and Diffusion-Tensor MRI can be used for tumor classification. Apart from that, it is known that brain contains three primary tissues viz. White Matter (WM), Gary Matter (GM) and Cerebrospinal Fluid (CSF); so, these regions of interest (ROI) can be farther detected with the help of Segmentation.

REFERENCES

[1] M. M. Ahmed and D. B. Mohammad, "Segmentation of brain mr images for tumor extraction by combining kmeans clustering and perona-malik anisotropic diffusion model," IJIP, pp. 27-34, 2008.

[2] J. Selvakumar, A. Lakshmi, and T. Arivoli, "Brain tumor segmentation and its area calculation in brain mr images using k-mean clustering and fuzzy c-mean algorithm," in Proceedings in IEEE-International Conference On Advances In Engineering, Science And Management, 2012, pp. 186-190.

[3] G. Hamerly and C. Elkan, "Learning the k in k-means," in Proceedings i7th Annual Conference on Neural Information Processing Systems (NIPS), 2003, pp. 281-288.

[4] B. C. Patel and G. R. Sinha, "An adaptive k-means clustering algorithm for breast image segmentation," International lournal of Computer Applications, vol. 7, no. 4, pp. 35-38, 2010.

[5] T. Kalaiselvi and K. Somasundaram, "Fuzzy c-means technique with histogram based centroid initialization for brain tissue segmentation in mri of head scans," in Proceedings in IEEE-international Symposium on Humanities, Science and Engineering Research, 2011, pp. 149-154.

[6] M. Shasidhar, Y. S. Raja, and B. Y. Kumar, "Mri brain image segmentation using modified fuzzy c-means clustering algorithm," in Proceedings in IEEE-International Conference on Communication Systems and Network Technologies, 2011, pp. 473-478.

[7] S. Koley and A. Majumder, "Brain mri segmentation for tumor detectionusing cohesion based self merging algorithm," in Proceedings IEEE-3rd International Conference on Communication Software and Networks, 2011, pp. 781-785.

[8] M. U. Akram and A. Us man, "Computer aided system for brain tumor detection and segmentation," in Proceedings iEEE-International Conference on Computer Networks and Information Technology, vol. 1,2011, pp. 299-302.

[9] K. Thapaliya and G. Kwon, "Extraction of brain tumor based on morphological operations," in Proceedings iEEE-8th international Conference on Computing Technology and Information Management, 2012, pp. 515-520.

[10] M. Schmidt, I. Levner, R. Greiner, A. Murtha, and A. Bistritz, "Segmenting brain tumors using alignmentbased features," in Proceedings iEEE-4th International Conference on Machine Learning and Applications, 2005.

[11] A. Elamy and M. Hu, "Mining brain tumors and tracking their growth rates," in Proceedings IEEE-Canadian Conference On Electrical and Computer Engineering, Seaside, CA, USA, 2007, pp. 872-875.

[12] B. Chanda and D. D. Majumder, Digital Image Processing and Analysis, 2nd ed. PHI Learning Pvt. Ltd, 2011.