An MRI Scanner measures the radio frequency emitted by hydrogen atoms after a first step during which this absorbs energy to the same radio frequency this effect is related to the necessity of an atom to return to an equilibrium state after an excitation. īOLD signal in fMRI uses the hydrogen atoms that compose the water molecules contained in the brain to study its activity. This approach uses the BOLD signal in fMRI to study the activity of the hydrogen atoms that compose the water molecules contained in the brain. For instance, in neuroimaging an interesting branch of study concerns the correlation between brain regions and cognitive functions using several techniques such as the Functional-MRI (fMRI) the latter is based on the Blood Oxygenation Level Dependent (BOLD) signal which represents the increment of blood oxygenation in order to identify the brain region affected during activities (e.g., hands motion) and to reflect the local neuronal signaling. Bioinformatics techniques allow the developing of solutions which provide a support for images acquisition and analysis as well as the integration of biological datasets (e.g., gene data and ontologies) for patterns discovering and disorders detection.

These different approaches make that a CT scanner is better suited to detect cancer or cerebral hemorrhage, while brain tumor is more clearly visible using an MRI scanner. An MRI Scanner measures the RF emitted by hydrogen atoms, the RF are directly related to the amount of energy previously provided to the atom and to its necessity to return at an equilibrium state after an excitation. The term “bioimage” concerns all images related to biological samples acquired using medical technologies such as the Computed Tomography (CT) or the Magnetic Resonance Imaging (MRI) briefly, CT is a technique based on ionizing radiation, for instance, X-rays are used to acquire the images instead, MRI (or Nuclear-MRI) uses magnetic fields and Radio Frequencies (RF) to produce detailed pictures. In the last years, medical and biological images are quickly growing in terms of size and information content. Bioinformatics tools allow to retrieve information able to support a scientist during a diagnosis in order to detect efficiently abnormalities and to monitor their changes over time. The use of sophisticated devices has greatly improved the acquisition of data at a very high resolution and faster rate, although the image interpretation process based on machine learning techniques is only recently taking hold. The image data processing is a relevant support for clinical diagnosis and sharing of health information, for instance, the correct interpretation of images may be crucial for early diseases detection. In medicine, computational platforms generate high amount of data from medical devices such as Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) this chapter will survey on bioinformatics solutions and toolkits for medical imaging in order to suggest an overview of techniques and methods that can be applied for the imaging analysis in medicine. In addition, bioinformatics image analysis may be extended to support several scenarios, for instance, in cyber-security the biometric recognition systems are applied to unlock devices and restricted areas, as well as to access sensitive data.

Bioinformatics solutions provide an effective approach for image data processing in order to retrieve information of interest and to integrate several data sources for knowledge extraction furthermore, images processing techniques support scientists and physicians in diagnosis and therapies. In recent years, the increasing use of medical devices has led to the generation of large amounts of data, including image data.

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