Medical image analysis with artificial neural networks

J. Jiang, P. Trundle, J. Ren
2010 Computerized Medical Imaging and Graphics  
Given the fact 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 computeraided diagnosis, medical image segmentation and edge detection toward visual content analysis, and medical image registration for its pre-processing and post processing, with the aim of increasing awareness of how neural networks can be applied to these areas and providing a foundation for further
more » ... rch 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 all neural networks is included to provide a global view on computational intelligence with neural networks in medical imaging. Indexing terms: neural networks, medical imaging analysis, and intelligent computing. 2 Neural network applications in computer-aided diagnosis represent the main stream of computational intelligence in medical imaging [1~14]. Their penetration and involvement are almost comprehensive for all medical problems due to the fact that: (i) neural networks have the nature of adaptive learning from input information and, using a suitable learning algorithm, can improve themselves in accordance with the variety and the change of input content; (ii) neural networks have the capability of optimising the relationship between the inputs and outputs via distributed computing, training, and processing, leading to reliable solutions desired by specifications; (iii) medical diagnosis relies on visual inspection, and medical imaging provides the most important tool for facilitating such inspection and visualization. Medical image segmentation and edge detection remains a common problem and foundational for all medical imaging applications [15~25]. Any content analysis and regional inspection requires segmentation of featured areas, which can be implemented via edge detection and other techniques. Conventional approaches are typified by a range of well researched algorithms, including watershed [15], snake modelling [16], regiongrowing [17, N26], and contour detection etc [52]. In comparison, neural network approaches exploit the learning capability and training mechanism to classify medical images into content consistent regions to complete segmentations as well as edge detections [23-25]. Another fundamental technique for medical imaging is registration, which plays important roles in many areas of medical applications [26~32]. Typical examples include wound care, disease prediction, health care surveillance and monitoring etc. Neural networks can be designed to provide alternative solutions via competitive learning, self-organising and clustering to process input features and find the best possible alignment between different images or data sets. Other applications of ANN include data compression [33-38, 41], image enhancement and noise suppression [39, 40, 43, 44], and disease prediction [N12, N16] etc. More recently, application of ANN for functional magnetic resonance imaging (MRI) simulation becomes a new research hotspot, where certain structured ANNs are employed to simulate the functional connectivity of brain networks [N1, N6]. Due to the similar nature of ANN and human neurons, ANN has been proved to be a very useful for this new task [N23, N34]. To provide useful insights for neural network applications in medical imaging and computational intelligence, we structure the rest of this paper in six further sections, where Section 2 provides some basics about neural networks to enable beginners to understand the structure, the connections, and the neuron functionalities. After this section, four sections are organised to provide detailed descriptions of neural network applications in the areas of computer aided diagnosis, image segmentation and edge detection, image registration, and other applications. Finally, conclusion and discussions are covered in section 7, providing concluding remarks to complete the paper. Neural Networks Fundamentals
doi:10.1016/j.compmedimag.2010.07.003 pmid:20713305 fatcat:iycrdoy4yfgjfof2ml4xk7iz6i