A new framework for the integrative analytics of intravascular ultrasound and optical coherence tomography images

Abstract:The integrative analysis of multimodal medical images plays an important role in the diagnosis of coronary artery disease by providing additional comprehensive information that cannot be found in an individual source image. Intravascular ultrasound (IVUS) and optical coherence tomography (IV-OCT) are two imaging modalities that have been widely used in the medical practice for the assessment of arterial health and the detection of vascular lumen lesions. IV-OCT has a high resolution and poor penetration, while IVUS has a low resolution and high detection depth. This paper proposes a new approach for the fusion of intravascular ultrasound and optical coherence tomography pullbacks to significantly improve the use of those two types of medical images. It also presents a new two-phase multimodal fusion framework using a coarse-to-fine registration and a wavelet fusion method. In the coarse-registration process, we define a set of new feature points to match the IVUS image and IV-OCT image. Then, the improved quality image is obtained based on the integration of the mutual information of two types of images. Finally, the matched registered images are fused with an approach based on the new proposed wavelet algorithm. The experimental results demonstrate the performance of the proposed new approach for significantly enhancing both the precision and computational stability. The proposed approach is shown to be promising for providing additional information to enhance the diagnosis and enable a deeper understanding of atherosclerosis

A robust adaptive wavelet-based method for classification of meningioma histology images

Intra-class variability in the texture of samples is an important problem in the domain of histological image classification. This issue is inherent to the field due to the high complexity of histology image data. A technique that provides good results in one trial may fail in another when the test and training data are changed and therefore, the technique needs to be adapted for intra-class texture variation. In this paper, we present a novel wavelet based multiresolution analysis approach to meningioma subtype classification in response to the challenge of data variation.We analyze the stability of Adaptive Discriminant Wavelet Packet Transform (ADWPT) and present a solution to the issue of variation in the ADWPT decomposition when texture in data changes. A feature selection approach is proposed that provides high classification accuracy

Model for Estimation of Bounds in Digital Coding of Seabed Images

This paper proposes the novel model for estimation of bounds in digital coding of images. Entropy coding of images is exploited to measure the useful information content of the data. The bit rate achieved by reversible compression using the rate-distortion theory approach takes into account the contribution of the observation noise and the intrinsic information of hypothetical noise-free image. Assuming the Laplacian probability density function of the quantizer input signal, SQNR gains are calculated for image predictive coding system with non-adaptive quantizer for white and correlated noise, respectively. The proposed model is evaluated on seabed images. However, model presented in this paper can be applied to any signal with Laplacian distribution

Learning a Dilated Residual Network for SAR Image Despeckling

In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is based on dilated convolutions, which can both enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure. In addition, skip connections and residual learning strategy are added to the despeckling model to maintain the image details and reduce the vanishing gradient problem. Compared with the traditional despeckling methods, the proposed method shows superior performance over the state-of-the-art methods on both quantitative and visual assessments, especially for strong speckle noise.Comment: 18 pages, 13 figures, 7 table

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin