The recent developments of information technology have enhanced the Picture Archiving and Communication System. There are developments in mobile devices, cloud computing technology and intelligence method in the PACS. This chapter elaborates the advancement that has been made and explores their impacts.

In this chapter, a model of secured mobile system is described for image distribution in hospitals. In this system, it consists of mobile application server and mobile application client. The mobile application server communicates with hospital PACS and the client is embedded in the end-user’s smart phone. The IMEI code of the mobile phone is sent by SMS to designated user to ensure security. The system can manage DICOM images with image processing capability with reasonable image quality. The mobile PACS demonstrates a model of using smart phone to improve the efficiency of health care services by speedy delivery of image information.

The medical images in the Picture Archiving and Communication System (PACS) provide a resource for data mining and knowledge discovery. The information in the DICOM header can be extracted to produce meaningful information. We illustrate with two examples in the practice of medical imaging.

Computer aided diagnosis or detection is a computer system to assist medical doctors to improve the accuracy and efficiency of health care. We describe a computer-aided detection method for stroke as an example to illustrate the steps describing this method.

Background: The image registration using positron emission tomography (PET) and magnetic resonance imaging (MRI) has been studied extensively. The purpose of this project is to propose a patient-specific image registration model that can improve both the accuracy and efficiency of the registration process. Large-scale deformation of the breast makes image registration of the breast a challenging task. Usually, a patient undergoes MRI in the prone position and PET in the supine position. Registration of breast in supine position with breast in prone position is a challenge. Methods: Eight cases with corresponding pairs of PET/computed tomography (CT) and MRI breast images were used in this study for the performance of PET/MRI registration. Registration was based on a biomechanical finite element model that could simulate large-scale deformation of the breast under pressure. Results: In this study, an accurate patient-specific registration model was built with a target registration error (TRE) of 4.77±2.20 mm. Conclusion: Image deformation due to the effect of gravity was successfully modeled by the finite element method.

Alzheimer disease (AD) is a neurological disorder characterized by mild cognitive impairment and affects an individual’s quality of life. In this chapter, we explore two sets of advanced imaging analytic tools for quantitative detection of the important indicators of AD such as presence of White Matters Hyperintensities and diminished hippocampus. Case studies are used to further illustrate the scope of the use of these tools.

Bone is a composite tissue comprised of organic and inorganic phases. It adapts itself to mechanical strains with an aim to maintain its mechanical competence via modelling and remodeling. Such adaptation of bone can result in alteration of material and structural properties including bone mineral density (BMD), microarchitecture, mineralization, and morphology. Quantitative bone imaging enables the evaluation of bone status in relation to diseases, mechanical and other interventions. However, bone quantity measurement of BMD using dual-energy X-Ray absorptiometry is limited because of its projection imaging approach and provides only a scalar measurement. As an anisotropic material, other bone quality including the architecture and spatial distribution of bone have to be considered in the evaluation of bone status. Also collagen fiber orientation and degree of mineralization in this composite tissue are important determinants of bone adaptation in response to treatment interventions. Thus, synergized use of multi-imaging modalities may decipher the interplay of material and structural properties in bone adaptation. Current imaging techniques using peripheral quantitative computed tomography, microcomputed tomography, magnetic resonance imaging, quantitative ultrasound and circularly polarized light microscopic imaging have gone beyond the measurement of bone quantity and to provide significant information of bone quality in the understanding of bone status. The present chapter aims to discuss the contribution of different imaging modalities in the evaluation of bone status.

This chapter introduces a computer-aided method to detect skin lesion using image features and shape features. Artificial neural networks (ANNs) trained with image features (energy, contrast, homogeneity and correlation) and shape features (asymmetry, border irregularity, color and diameter) in differentiating common nevus, atypical nevus and melanoma using dermoscopy images were described. 120 dermoscopy skin lesion images were collected from online PH2 database. The model was built on a single 3 layers, feed forward back propagation ANNs trained and tested with round robin method. The ANN’s performance was evaluated with receiver operating characteristic (ROC) analysis and chi-square test. The performance was evaluated by comparing total dermoscopy score method with ANNs method. Our result noted that the area under curve (Az) of ROC were 0.807 for differentiating atypical nevus from common nevus, 0.998 for differentiating melanoma from common nevus and 0.959 for i differentiating melanoma from atypical nevus, respectively. This indicated that the ANNs method provided an accurate differential diagnosis in common skin lesions for dermoscopy images.