IMPLEMENTATION OF DIFFERENTIAL EVOLUTION ALGORITHM TO PERFORM IMAGE FUSION FOR IDENTIFYING BRAIN TUMOR

Automated mechanization for curing a disease is a reliable and protuberant method. A disease in brain can be detected by Magnetic Resonance Imaging (MRI). In this context, image fusion is a method for creating an image by merging pertinent data from 2 or more images. The resultant image will be highly useful than the individual input images to retentive the vital characteristics of every image. Multiple image fusion is a significant method employed in image processing techniques. In this study, differential evolution (DE) algorithm-based image fusion has been performed with MRI and computed tomography (CT) images. The simulation works have been carried out to evaluate the different quality measurements of DE on image fusion.


INTRODUCTION
Brain tumours is harmful to humans, due to the atypical availability of cells inside the brain. Brain function will be interrupted and be deadly. Benign and malignant tumors are frequently identified. Benign tumors are not as harmful as malignant tumors, because they can grow rapidly. Medical imaging methodologies such as MRI, CT, Ultrasound, X-ray etc. are employed to display the internal body parts for diagnosing (Rowden, 2019).
Among them MRI is widely employed and it offers accurate brain images and cancer cells.
So, brain tumor can be detected via MRI images. This study concentrates on detection of brain tumor through image fusion. Image fusion is a process of merging two or more images into a single compound image that contains the information of the source images without clamor. Multi-modular recuperative image fusion has been employed to recognize the wounds. In biomedical image processing image fusion has got more attention in the past decade (Daneshvar & Ghassemian, 2010;Wang, Li, & Tian, 2014). MRI and CT images held more practical information than biomedical images. The aim of image fusion is to obtain the information at each pixel without damaging the pixel associations of the particular image.
In this context, previously, a complex wavelet modification for image fusion has been proposed to attain the optimal combination using Lifting wavelet transform (LWT), Multiwavelet transform (MWT), Stationary wavelet transform (SWT) and spatial domain (Principal component analysis (PCA) approaches (Singh & Khare, 2014). Similarly, undecimated wavelet has been implemented, where the image is crumbled into two successive scrutinizing errands (Ellmauthaler et al., 2013). An affable fusion technique using SWT and NSCT has been presented, where the input image is rotten by SWT and NSCT. The coefficients of SWT and NSCT are combined to form the fused image (Li & Liu, 2009). A new framework has been proposed where the images considered with SWT primarily and the overall textural topographies have been attained via gray level cooccurrence matrix (Singh & Khare, 2014;Huang et al., 2014;Shi & Fang, 2007  Primarily, the informative source images such as CT and MRI images have been collected.
Subsequently, the source images have been converted into dark scale and resized. The enhancement of quality of the images has been performed using imadjust order available in MATLAB simulation. Commotion dismissal has been carried out by using median channel.
This is an excellent method in ejecting salt and pepper commotions of biomedical images.
It happens due to the movement of antiquities. (1) Contrast reinstates the data associated with the pixel with the adjacent pixel. It has been calculated as follows.
(3)   Homogeneity has been used to estimate the intimacy of components availed in gray level concurrence matrix (GLCM).

DIFFERENTIAL EVOLUTION ALGORITHM
Price and Storn introduced DE as a population-based stochastic direct search technique.
The implementation procedure of DE has been adopted from Aslantas and Toprak (2014). The steps involved in DE based image fusion have been illustrated in Figure 2. The best control parameters for DE have been provided in Table 1.
The performance indices such as MSE, PSNR, Contrast, Entropy and Homogeneity have been presented in Table 2.

RESULTS AND DISCUSSIONS
MRI and CT images have been fused together using DE. The ultimate objective of image fusion is to acme the valuable data from various input images. The adaptive fuzzy clustering rule has been employed to fragment the region of interest (ROI) to isolate the tumor from the resultant fused image. It will group the various grade intensity segments of the fused image. The segments with huge grade intensity are marked as the tumor, and they have been isolated using thresholding method (Chabira, Skanderi, & Aichouche, 2013).

CONCLUSION
A typical muscles grown in brain disturb brain activities and that is referred as brain tumor.
Biomedical image processing aims to recognize precise data through images with minimum error. Detection of brain tumor via MRI images is not easy due to the intricacy of brain. A pixel based image fusion procedure using DE-DWT has been proposed in this study. The simulations have been carried out with CT and MRI images. The performance indices such as entropy, MSE, PSNR, contrast and homogeneity imply the effectiveness of the proposed DE-DWT approach.