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 region-based Principal Component Analysis (PCA) technique for medical image compression

Principal Component Analysis (PCA) is capable of completely decorrelating input data in the transform domain. However, PCA is limited in image compression because there is a need to encode the eigenvectors of the input data and thereby affects the rate-distortion performance. In an effort to improve rate-distortion performance, this work proposed a block-to-row PCA (BTRPCA) algorithm that employs the eigenvectors from the model image of the same image modality coupled with a row vectorization approach. Region-based compression schemes that reduce storage space while preserving the image quality of the region of interest (ROI) are receiving attention due to the increase in medical imaging data. While PCA is inherently limited by its matrix form, the Arbitrary ROI coding (ARC) proposed in this work models the ROI by means of a factorization approach and the arbitrary-shaped ROI contours and NROI are compressed using BTRPCA. In order to minimize user interaction, an automated brain segmentation technique based on midsagittal plane (MSP) and Absolute Difference Map (ADM) is then incorporated into the proposed Automated Arbitrary PCA (AAPCA). The presented result showed that BTRPCA achieves PSNR improvements of up to 10 dB compared to its PCA counterparts. The ARC outperforms JPEG, Embedded Zerotree Wavelet (EZW) and Embedded Block Coding With Optimized Truncation (EBCOT) at all tested bit rates with an average PSNR improvements of 6 dB, 18 dB and 12 dB respectively. Subjective performance analysis was in agreement with the objective performance analysis in which the AAPCA is capable of extending beyond the compression limits of the conventional PCA algorithm and that the quality of the surroundings of ROI is degrading gracefully at bpp as low as 0.25. The research has successfully developed an improved region-based compression scheme for medical images where lossy and lossless compression is implemented in one PCA architecture. Continuation of this study include using different encoding schemes to boost the rate-distortion performance and extraction of multiple ROI