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In the recent past, a medical 2D image fusion approach has played a vital role in the biomedical industry for diagnosing disease using various modalities of medical images. Biomedical 2D image fusion synthesis is a combining technique that merges useful information from two or more modalities of data captured through high definition scanning devices. The fused image is highly useful when doctors make emergency surgical decisions for their clinical patients. In this research, an innovative medical 2D image fusion approach called 2-Dimensional Double Density Wavelet Transform (2D-DDWT), based on maximization of scaling rule, is being proposed for the combination of CT and MRI images of human brain. The source image is applied to 2D-DDWT pursued by the fusion of sub images of coefficients based on high and low frequencies that are decomposed by transformation technique. The low frequency information is decomposed using the congruency rule of phase while the high frequency information is decomposed using 2D Gabor filter. The 2D-DDWT frequency coefficients are fused by using Principle Component Analysis (PCA) for the approximation parameters, and therefore the rule of selection maximum is being applied for the coefficients to improve features of image fusion such as segmentation. Maximization scaling rule is proposed for pixel level fusion to improve quality metrics. The quantitative and qualitative analysis proves the efficiency of the proposed methodology and demonstrates the improvement of the proposed methodology over existing methods.

Multimodal medical image fusion helps to acquire more information about both the functional and structural informations. More over storage problems can also be addressed properly when the fusion process is adopted. A hybrid image fusion is being sought for an optimum algorithm for a better quality image. This proposed work describes about the multimodal image fusion framework based on empirical mode decomposition of images and fusion by discrete wavelet transform method. The obtained fused image by the proposed method consists of all the functional information and also the spatial characteristics of original image. No distortion is also present in the fused image. In this proposed work Magnetic Resonance Imaging (MRI), and Computerized Tomography (CT) images of brain are used for fusion. The fusion results obtained by this method are observed and quantitatively analyzed. The hybrid fusion response of this approach depicts the dominance of the acquired results.

The storage facility of image and image transfer is an extensive appliance in image compression. Image compression techniques require the proper and effective transforms and encoding methods to reach the aim. In this work, discrete wavelet transform based image compression algorithm is used for decomposing the image. The effectiveness of different Encoder loops are analyzed based on the values of peak signal to noise ratio (PSNR), compression ratio (CR), means square error (MSE) and bits per pixel (BPP). The optimum encoding loop for compression is also found based on the results.

Image fusion technique has been widely in use in many applications such as computer vision, surveillance, robotics, medical imaging, remote sensing etc. Medical image fusion plays a vital role to deal with medical issues reflected through images of human body, organs and cells. It is necessary to preserve the fused image in order to keep the relevant information of input images. It is also necessary to keep the fusion process away from introducing any artifacts or inconsistencies. More over image fusion techniques must provide a good quality and must enhance the applications of recent data. This paper elucidates the survey of recent literatures, which dealt medical images like magnetic resonance imaging (MRI), Computed Tomography (CT), Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) of brain. This paper reviews some of the recent medical image fusion algorithm such as Non Sampled Contourlet transform, Directive Contrast, Daubechies complex wavelet transform, Shift-invariant Shearlet transform etc. The merits and demerits of the various image fusion algorithm which developed by the authors is described in the paper. The survey emerges out the optimum fusion algorithm.