# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 3 General Procedure of Medical Image Fusion  
Image fusion technology is mainly applied to solve the limitations of single-modal image guidance, including optical, medical and electromagnetic tracking imaging [7]. The medical fusion method contains two stages: image registration and the fusion of relevant features from the registered image. Registration of different imaging modals is performed using external sensor coils, internal references, or anatomical markers [8]. The registration of the images requires a method to correct the spatial misalignment between the multimodal images that often results from scale changes, rotationsand translations. This step matches the input images using their characteristics in order to facilitate the image fusions. The next stage is to find some rules to integrate multiple input images into one comprehensive image. The medical image can be fused by each pixel, feature extraction, region segmentation and marker point determination of anatomical structure or lesion condition. After fusion is complete, the operator interface often displays with original and overlapping cross-sectional images side-by-side. The fused image can help doctors to make accurate decisions for various diagnoses. For the efficient treatment of liver tumors, information on tumor size, location and number can be obtained by the fusion method accurately. Compared with ultrasound technology used previously, the development of new image fusion technology has greatly improved diagnostic accuracy [9]. For instance, image fusion guidance technology has been widely used in thermal ablation therapy in which two-dimensional Ultrasonography does not clearly show liver cancer lesions. This method uses high-contrast CT/MRI along with real-time guidance and the evaluation of ablation borders via Ultrasonography to demonstrate clear liver cancer lesions. More examples of medical image fusion applied in liver tumor diagnosis and treatment will be further discussed in Section 5. In these procedures, image registration across modalities is important, and could highly impact the qualities of image fusion. As a result, we briefly introduce the medical image registration which mainly contains four steps:  
1. Feature extraction: The first step of image registration is to extract image features (feature descriptor), such as feature points/edges/contours/areas/structures, from input images. 2. Feature matching: The second step of image registration is feature matching. It is used to find the correspondence between the extracted features from Step 1. 3. Determination of geometric transformation parameters: This is the most important step in the image registration procedure. Based on the correspondence between the extracted features from Step 2, a suitable geometric transformation model is selected. Then, based on a certain measurement function, the geometric transformation parameters are determined. The commonly used geometric transformation models are: * Rigid transformation, which is mainly a transformation for rotation and translation. * Similarity transformation, which is mainly a transformation for translation, rotation and scaling. * Affine transformation, which is mainly a transformation for translation, rotation, scaling and shearing * Projective transformation, which is a combination of transformations, such as translation, rotation, scaling and shearing. 4. Image resampling and registration: Then, with appropriate interpolation function, a floating image is mapped to the reference image's coordinate space to finish the image registration (floating and reference images are defined as the input images to be registered). Among the above steps, the optimization of geometric transformation parameters in Step 3 is crucial for the quality of image registration. A function is often used to measure the similarity between the floating and reference images.