Detection of Brain Tumour Using Brightness Preserving Dynamic Fuzzy Histogram Equalization

International Journal of Electrical, Electronics and Computer Systems (IJEECS) ______

Detection of Brain Tumour using Brightness Preserving Dynamic Fuzzy Histogram Equalization

1Hemant Kaur, 2Gurmeet Kaur, 3Beant Kaur 1M. Tech Student, Department of Electronics & Communication Engineering, Punjabi University, Patiala, India 2Faculty, Department of Electronics & Communication Engineering, Punjabi University, Patiala, India 3Faculty, Department of Electronics & Communication Engineering, Punjabi University, Patiala, India Email: [email protected]

ABSTRACT:In this research paper we proposed a novel processing and analysis applications. Mathematical technique for detection of brain tumour by using morphology denotes a branch of biology that deals with brightness preserving dynamic fuzzy histogram the forms and structures of animals and plants. equalization. The fuzzy histogram equalization uses fuzzy statistics of digital images for their representation. The Mathematical morphology is a tool for extracting image technique enables to handles the imprecision of gray level components that are useful in representation and value in better way resulting the highlight to hidden as well description of region shape, such as boundaries, as blur part of the images. The Brightness Preserving skeletons and convex hull. The technique was originally Dynamic Fuzzy Histogram Equalization(BPDFHE) enables developed by Mat heron and Serra at Cole des mines in us to use only two morphological operations for detection of brain tumour. Experimental result shows that the Paris. The language of mathematical morphology is set proposed method can effectively and significantly detect theory and sets in mathematical morphology represent the brain tumour. The proposed method has been tested objects in an image. It is also useful for pre and post using 6 gray scale images and gives better results as processing techniques. Mathematical morphology is a compared to the conventional methods. theory of image transformations and image functional. Keywords-Image Enhancement, Fuzzy Logic, Fuzzy Image Morphological operations are based on simple Processing, GHE, BPDHE, BPDFHE. expanding and shrinking operations. Mathematical morphology examines the geometrical structure of an I. INTRODUCTION image by probing it with small patterns, called structuring element, of varying sizes and shapes. This Histogram equalization is a method in image procedure results in non-linear image operators which processing of contrast adjustment using the image’s are well suited to exploring geometrical and topological histogram. This method usually increases the structures. Different applications of mathematical global contrast of many images, especially when the morphology are Image Enhancement, Image usable data of the image is represented by close contrast Segmentation, Image Restoration, Edge Detection, values. The histogram shows how many times a texture analysis. It analyses the shapes and forms of particular grey level appears in an image. Histogram objects. In computer vision, it is used as a tool to extract equalization transforms the intensity values so that image components that are useful in the representation histogram of output image approximately matches the and description of object shape. It is mathematical in the uniform histogram[1]. For the purpose of image analysis sense that the analysis is based on set theory, topology, and pattern recognition there is always a need to lattice algebra, function, and so on [3-7]. Another use of transform an image into another better represented form. mathematical morphology is to filter image. It is well During the past five decades image processing known non-linear filter for image enhancement. techniques have been developed tremendously and mathematical morphology in particular has been Image Segmentation is separation of structures of continuously developing because it is receiving a great interest from the Background and each other. Another deal of attention because it provides a quantitative way of extracting and representing information from an description of geometric structure and shape and also a image is to group pixels together into regions of mathematical description of algebra, topology, similarity. Importance of Image segmentation: Fully probability, and integral geometry [2]. Mathematical automatic brain tissue classification from magnetic morphology is extremely useful in many image ______ISSN (Online): 2347-2820, Volume -2, Issue-5,6, 2014 1 resonance images (MRI) is of great importance for intensity of the input, thus fulfils the requirement of research and clinical studies of the normal and diseased maintaining the mean brightness of the image [10]. This human brain. Segmentation of medical imagery is a method is actually an extension to both MPHEBP and challenging problem due to the complexity of the DHE. Similar to MPHEBP, the method partitions the images. histogram based on the local maximums of the smoothed histogram. However, before the histogram II IMAGE CONTRAST ENHANCEMENT equalization taking place, the method will map each TECHNIQUES partition to a new dynamic range, similar to DHE. As There are many techniques available for image contrast the change in the dynamic range will cause the change enhancement; the techniques that use first order statistics in mean brightness, the final step of this method of digital images (image histogram) are very popular. involves the normalization of the output intensity. So, Global Histogram Equalization (GHE) [1] is one such the average intensity of the resultant image will be same widely used technique. GHE is employed for its as the input. With this criterion, BPDHE will produce simplicity and good performance over variety of images. better enhancement compared with MPHEBP, and better However, Global Histogram in preserving the mean brightness compared with DHE Equalization(GHE)introduces major changes in the [11]. image gray level when the spread of the histogram is not III FLOW OF ALGORITHM significant and cannot preserve the mean image- brightness which is critical to consumer electronics The flowchart for detection of Brain Tumour using applications. Its limitations are overcome by dynamic Brightness Preserving Dynamic Fuzzy Histogram histogram equalization. The Dynamic Histogram Equalization is as follow. Equalization (DHE) technique takes control over the effect of traditional Histogram Equalization so that it performs the enhancement of an image without making any loss of details in it. DHE divides the input histogram into number of sub-histograms until it ensures that no dominating portion is present in any of the newly created sub-histograms. Then a dynamic gray level (GL) range is allocated for each sub-histogram to which its gray levels can be mapped by Histogram Equalization. This is done by distributing total available dynamic range of gray levels among the sub-histograms based on their dynamic range in input image and cumulative distribution (CDF) of histogram values. This allotment of stretching range of contrast prevents small features of the input image from being dominated and washed out, and ensures a moderate contrast enhancement of each portion of the whole image. At last, for each sub- histogram a separate transformation function is calculated based on the traditional HE method and gray levels of input image are mapped to the output image accordingly. The whole technique can be divided in three parts, partitioning the histogram, allocating GL ranges for each sub-histogram and applying histogram equalization on each of them [8].The Brightness Preserving Bi-Histogram Equalization(BBHE). This method divides the image histogram into two parts. In this method, the separation intensity construct the input image. After this separation process, these two histograms are independently equalized. By doing this, the mean brightness of the resultant image will lie between the input mean and the middle gray level [9]. The histogram with range from 0 to L-1 is divided into two parts, with separating Intensity XT. This separation produces two histograms. The first histogram has the range of 0 to XT, while the second histogram has the range of XT+1 to L-1.The brightness preserving dynamic histogram equalization (BPDHE), which is an extension to Histogram Equalization that can produce the output image with the mean intensity almost equal to the mean International Journal of Electrical, Electronics and Computer Systems (IJEECS) ______

In this section, we present some experimental results of our proposed method, together with Global histogram equalization (GHE), Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDHE) for comparison. The original image, together with the results based on GHE, BPDHE and BPDFHE, are shown in Fig.2 to Fig. 7Here (a) represents the original image,(b)represents the GHE image,(c)represents the BPDHE image and (d) represents the BPDFHE image.

(a) (b)

(c) (d) Fig. 2(a) Shows the original image (b) Shows the segmented image using GHE (c) Shows the segmented image using BPDHE (d) Shows the segmented image using BPDFHE

Fig. 1 Flow of algorithm (a) (b) In this algorithm in first step Fuzzy Histogram Computation is done using delta membership function. Fuzzy statistics is able to handle the inexactness of gray values in a better way. The next step involves Partitioning of the Histogram. It includes Detection of Local Maxima and further partitions are created. In next step Dynamic histogram equalization takes place which includes mapping partitions to dynamic range and equalizing each sub histogram. The next step involves Normalization of image histogram. In next step the (c) (d) Region based edge detection is done. Next we find gradient of the image. Then we perform erosion and Fig. 3(a) Shows the original image (b) Shows the dilation over the image and as a output we get segmented image using BPDHE (c) Shows the segmented image[12]. segmented image using GHE (d) Shows the segmented image using BPDFHE IV. EXPERIMENTAL RESULTS

______ISSN (Online): 2347-2820, Volume -2, Issue-5,6, 2014 3 (a) (b) (c) (d) Fig. 6 (a) Shows the original image (b) Shows the segmented image using BPDHE (c) Shows the segmented image using GHE (d) Shows the segmented image using BPDFHE

(c) (d) Fig.4 (a) Shows the original image (b) Shows the segmented image using GHE (c) Shows the segmented image using BPDHE (d) Shows the segmented image using BPDFHE. (a) (b)

(a) (b) (c) (d) Fig. 7 (a) Shows the original image (b) Shows the segmented image using BPDHE (c) Shows the segmented image using GHE (d) Shows the segmented image using BPDFHE Discussion: Above figures shows the results of different histogram equalization techniques namely GHE,BPDHE and BPDFHE and it can be concluded that the results of (c) (d) BPDFHE are much better than the traditional methods of GHE and BPDHE. Moreover, our proposed Fig. 5 (a) Shows the original image (b) Shows the algorithms are having good segmentation results than segmented image using BPDHE (c) Shows the GHE and BPDHE. segmented image using GHE (d) Shows the segmented image using BPDFHE TABLE I : COMPARISON OF DIFFERENT METHODS USING PSNR PARAMETER.

IMAGE GHE BPDHE BPDFHE IMAGE 1 11.8602 23.9681 30.0147 IMAGE 2 9.7801 21.3609 29.9681 IMAGE 3 9.9681 21.1250 28.7805 (a) (b) IMAGE 4 10.5670 22.3459 29.5631 IMAGE 5 10.4406 22.4506 27.2308 International Journal of Electrical, Electronics and Computer Systems (IJEECS) ______

[5] Erdoğan Aldemir, Nerhun Yildiz, “Design of an IMAGE 6 11.3490 23.4512 30.6513 Automatic Target Recognition Algorithm”, Discussion: The above table shows comparison of IEEE,2013. various techniques using PSNR. Here, we can see that [6] Suman Rani, Beant Kaur, Deepti Bansal, PSNR is increasing. It means that by using our proposed “Detection of Edges Using Image Processing”, method our peak signal to noise ratio is increasing and International Conference on Emerging this gives the best result. Technologies in Electronics and V CONCLUSION Communication,2013. In this paper a novel technique for detection of brain [7] J.Serra, Ed., “Image Analysis and Mathematical tumourhas been proposed using brightness preserving Morphology”, vol. 2: Theoretical Advances, New dynamic fuzzy histogram equalization. We have tested York Academic, 1988 our algorithm over 6 gray scale images, and 100% [8] M. Abdullah-Al-Wadud, Md. Hasanul Kabir, M. segmentation is achieved. This shows that our proposed Ali Akber Dewan, and Oksam Chae , “A technique is more efficient than the any other technique dynamic histogram equalization for image existing in literature. contrast enhancement”, IEEE Trans. Consumer REFERENCES Electron, vol. 53, no. 2, pp. 593- 600, 2007. [9] Yeong-Taeg Kim , “Contrast enhancement using [1] R. C. Gonzalez and R. E. Woods, Digital Image brightness preserving Bi-Histogram Processing, 2nd edition, Prentice Hall, 2002. equalization”, IEEETrans. Consumer Electronics, [2] Xianghua Hou, Honghai Liu, “WeldingImage vol. 43, no. 1, pp. 1-8, 1997. Edge Detection and Identification Research [10] A. Jayachandran, R. Dhanashakeran et Based on Canny Operator”,International al. ,“Fuzzy Information System based Digital Conference on Computer Science and Service Image Segmentation by Edge Detection”, IEEE, System,2012. 2010. [3] Weifeng Zhong, Chaoqun Qin ,Chengji Liu, [11] H. Ibrahim, and Nicholas Sia Pik Kong , Huazhong Li,Hongmin Wang, “The Edge “Brightness preserving dynamic histogram Detection of Rice Image Based on Mathematical equalization for image contrast enhancement”, Morphology and Wavelet Packet”,Intemational IEEETrans. Consumer Electronics, vol. 53, no. 4, Conference on Measurement, Information and pp. 1752 – 1758, 2007. Control (MIC),2012. [12] D. Sheet and H. Garud, “Brightness Preserving [4] Zhonghai li, zhihui yang, wenlong wang, jianguo Dynamic Fuzzy Histogram Equalization”,IEEE cui , “An adaptive threshold edge detection Trans., Consumer Electronics, vol. 56, no. 4, method based on the law of gravity” IEEE,2013. Nov. 2010.

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