Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

A roadside units positioning framework in the context of vehicle-to-infrastructure based on integrated AHP-entropy and group-VIKOR

The positioning of roadside units (RSUs) in a vehicle-to-infrastructure (V2I) communication system may have an impact on network performance. Optimal RSU positioning is required to reduce cost and maintain the quality of service. However, RSU positioning is considered a difficult task due to numerous criteria, such as the cost of RSUs, the intersection area and communication strength, which affect the positioning process and must be considered. Furthermore, the conflict and trade-off amongst these criteria and the significance of each criterion are reflected on the RSU positioning process. Towards this end, a four-stage methodology for a new RSU positioning framework using multi-criteria decision-making (MCDM) in V2I communication system context has been designed. Real time V2I hardware for data collection purpose was developed. This hardware device consisted of multi mobile-nodes (in the car) and RSUs and connected via an nRF24L01+ PA/LNA transceiver module with a microcontroller. In the second phase, different testing scenarios were identified to acquire the required data from the V2I devices. These scenarios were evaluated based on three evaluation attributes. A decision matrix consisted of the scenarios as alternatives and its assessment per criterion was constructed. In the third phase, the alternatives were ranked using hybrid of MCDM techniques, specifically the Analytic Hierarchy Process (AHP), Entropy and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The result of each decision ranking was aggregated using Borda voting approach towards a final group ranking. Finally, the validation process was made to ensure the ranking result undergoes a systematic and valid rank. The results indicate the following: (1) The rank of scenarios obtained from group VIKOR suggested the second scenario with, four RSUs, a maximum distance of 200 meters between RSUs and the antennas height of two-meter, is the best positioning scenarios; and (2) in the objective validation. The study also reported significant differences between the scores of the groups, indicating that the ranking results are valid. Finally, the integration of AHP, Entropy and VIKOR has effectively solved the RSUs positioning problems

A Comparative Study for 2D and 3D Computer-aided Diagnosis Methods for Solitary Pulmonary Nodules

Many computer-aided diagnosis (CAD) methods, including 2D and 3D approaches, have been proposed for solitary pulmonary nodules (SPNs). However, the detection and diagnosis of SPNs remain challenging in many clinical circumstances. One goal of this work is to investigate the relative diagnostic accuracy of 2D and 3D methods. An additional goal is to develop a two-stage approach that combines the simplicity of 2D and the accuracy of 3D methods. The experimental results show statistically significant differences between the diagnostic accuracy of 2D and 3D methods. The results also show that with a very minor drop in diagnostic performance the two-stage approach can significantly reduce the number of nodules needed to be processed by the 3D method, streamlining the computational demand

Robust semi-automated path extraction for visualising stenosis of the coronary arteries

Computed tomography angiography (CTA) is useful for diagnosing and planning treatment of heart disease. However, contrast agent in surrounding structures (such as the aorta and left ventricle) makes 3-D visualisation of the coronary arteries difficult. This paper presents a composite method employing segmentation and volume rendering to overcome this issue. A key contribution is a novel Fast Marching minimal path cost function for vessel centreline extraction. The resultant centreline is used to compute a measure of vessel lumen, which indicates the degree of stenosis (narrowing of a vessel). Two volume visualisation techniques are presented which utilise the segmented arteries and lumen measure. The system is evaluated and demonstrated using synthetic and clinically obtained datasets