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Uses of Image Processing in Medical Report Generation

The medical images such as Positron Emission Tomography-Computed Tomography (PET-CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), Computerized Axial Tomography (CAT) and X-ray etc are used to retrieve complementary information from fusion of multimodal medical images. The goal of image fusion is to combine information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or computer processing. CT scanner is used to produce a detailed image of structures inside the human body and it is useful for obtaining images of soft Tissues, the Pelvis, Blood Vessels, Brain, Abdomen, Bones and Lungs. PET scanner is used to make a diagnosis or to get more data about a health condition or finding out how effective current treatment is. It also reveals how a part of the patient’s body is functioning. The MRI scanner is used to provide information about interactions with nearby atoms with chemical structure of organic molecules. It provides images from different angles and construct a three dimensional image. As we know that the key point of multi-focus image fusion is to decide which portion of each image are in better focus than their respective counterparts in the associated images and then combine these regions to construct a well-focused image by certain fusion rules, which play an important role in different fusion strategies. A large number of research works have been done in the area of image fusion. Garg et. al. proposed a multilevel medical image fusion using wavelet transform. In the proposed method regions are used as basic features for representation and retrieval of information among medical images and it uses a region competition based level set segmentation rather than wavelet transform because coefficients of wavelet transform does not preserve information about edges and boundaries. Also it avoids direct use of edge detection algorithms such as sobel or canny as they give much importance to the edges.

Feature level image fusion algorithms have been also implemented by Kor and Tiwary as well as by Calhoun and Adall. In a fusion of features like edges and boundaries of input images has been done that is extracted using wavelet transform maxima criterion. The proposed method gives better result for image fusion as image contrast, average information content and detail information of fused images are increased. Calhoun proposed a method that describes how function and structure are related in the same region of the brain and tried to retrieve complementary information by utilizing temporal EEG information and spatial fMRI (Functional Magnetic Resonance Imaging). Piella and Heijmans gave a multi-resolution fusion algorithm that combines pixel and region based fusion. In this method multi-resolution decompositions are used to represent the input images at different scales and multimodal segmentation is done to partition the input images at these scales.

Wavelet transforms and Independent Component analysis has been used for better multimodal medical image fusion. In, Multimodal medical image fusion using redundant Discrete Wavelet Transform has been proposed. This method presents a fusion algorithm that combine pairs of multispectral magnetic resonance imaging such as T1, T2 and Proton Density brain images. This algorithms utilizes different features of redundant discrete wavelet transform, mutual information based non-linear registration and entropy information to improve performance. This algorithm preserves edge and component information and provides improved performance compared to existing discrete wavelet transform based fusion algorithms. Cui et. al. used both wavelet transform and independent component analysis. In this method each of CT images were decomposed by discrete wavelet transform and then ICA (Independent Component Analysis) were used to analyze the wavelet coefficients in different level for acquiring independent component analysis. At last fusion of CT images is done that provides more detailed information about soft tissues such as muscles and blood vessels. This method is more efficient than weighted average and laplacian pyramid method in image fusion. Apart from the fusion of medical images there are a number of methodologies that deals with the fusion of one dimensional medical signal with one or two dimensional medical signals. Kannathal, et. al. has proposed data fusion of cardiovascular and hemodynamic signals for cardiac health diagnosis. In the proposed method, fusion of ECG, blood pressure, saturated oxygen content and respiratory data has been done for achieving improved clinical diagnosis of patients in cardiac care units. This method uses fuzzy logic based data fusion and provides a better diagnosis of cardiac diseases than by using ECG only. Correa et. al. presented a fusion algorithm that uses fMRI, sMRI and EEG data. These modalities record brain structure and function at different scales and fusing information from such complementary modalities promises to provide additional information across brain network and changes due to disease. This method uses multimodal canonical correlation analysis (mCCA).

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