The author proposes a method for simultaneous registration and segmentation of multi-source images, using the multivariate mixture model (MvMM) and maximum of log-likelihood (LL) framework. Specifically, the method is applied to the myocardial segmentation combining the complementary information from multi-sequence (MS) cardiac magnetic resonance (CMR) images. For the image misalignment and incongruent data, the MvMM is formulated with transformations and is further generalized for dealing with the hetero-coverage multi-modality images (HC-MMIs). The segmentation of MvMM is performed in a virtual common space, to which all the images and misaligned slices are simultaneously registered. Furthermore, this common space can be divided into a number of sub-regions, each of which contains congruent data, thus the HC-MMIs can be modeled using a set of conventional MvMMs. Results show that MvMM obtained significantly better performance compared to the conventional approaches and demonstrated good potential for scar quantification as well as myocardial segmentation. The generalized MvMM has also demonstrated better robustness in the incongruent data, where some images may not fully cover the region of interest, and the full coverage can only be reconstructed combining the images from multiple sources.
In the literature, there is a research topic referred to as multivariate image analysis, which has been proposed to deal with images that have more than one measurement per pixel @cite_7 @cite_3 . This research has particularly focused on the three-channel (RGB) color images or multispectral and hyperspectral images, and the methods have been designed to efficiently compress the highly correlated data and project them onto a reduced dimensional subspace, for example using the principal component analysis @cite_1 . Multivariate image analysis considers only data, meaning for each pixel in one image there should be a corresponding pixel in the other images @cite_3 . However, in medical imaging the multi-source images commonly do not have this congruency, as the images acquired from different sources can be misaligned to each other, and the imaging field-of-views and resolution can vary greatly across different acquisitions. The images having such differences are referred to as images in this work.
@cite_3 @cite_1 @cite_7
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Abstract Nowadays, image analysis is becoming more important because of its ability to perform fast and non-invasive low-cost analysis on products and processes. Image analysis is a wide denomination that encloses classical studies on gray scale or RGB images, analysis of images collected using few spectral channels (sometimes called multispectral images) or, most recently, data treatments to deal with hyperspectral images, where the spectral direction is exploited in its full extension. Pioneering data treatments in image analysis were applied to simple images mainly for defect detection, segmentation and classification by the Computer Science community. From the late 80s, the chemometric community joined this field introducing powerful tools for image analysis, which were already in use for the study of classical spectroscopic data sets and were appropriately modified to fit the particular characteristics of image structures. These chemometric approaches adapt to images of all kinds, from the simplest to the hyperspectral images, and have provided new insights on the spatial and spectroscopic information of this kind of data sets. New fields open by the introduction of chemometrics on image analysis are exploratory image analysis, multivariate statistical process control (monitoring), multivariate image regression or image resolution. This paper reviews the different techniques developed in image analysis and shows the evolution in the information provided by the different methodologies, which has been heavily pushed by the increasing complexity of the image measurements in the spatial and, particularly, in the spectral direction. Information from on-line imaging sensors has great potential for the monitoring and control of spatially distributed systems. The major difficulty lies in the efficient extraction of information from the images in real-time, information such as the frequencies of occurrence of specific features and their locations in the process or product space. This paper uses multivariate image analysis (MIA) methods based on multiway principal component analysis to decompose the highly correlated data present in multispectral images. The frequencies of occurrence of certain features in the image, regardless of their spatial locations, can be easily monitored in the space of the principal components (PC). The spatial locations of these features in the original image space can then be obtained by transposing highlighted pixels from the PC space into the original image space. In this manner it is possible to easily detect and locate (even very subtle) features from real-time imaging sensors for the purpose of performing ... Multivariate image analysis (MIA) is a methodology for analyzing multivariate images, where the image coordinates are position (two- or three-dimensions) and variable number. Multivariate images ca ...