Imagej's contribution to left ventricular segmentation in myocardial perfusion imaging

Introduction: The Myocardial Perfusion Imaging (MPI) is a non-invasive image test that allows the assessment of perfusion, function, and viability of the Left Ventricle (LV). The quantitative parameters obtained post-reconstruction requires an accurate segmentation of the LV. ImageJ is an open-source software that provides segmentation techniques that may contribute to the segmentation of the LV in the MPI. The purpose of this study was to study the influence of the different segmentation methods provided by ImageJ, in MPI, depending on the administered activity. Material and methods: We carried out an experimental research with 4 MPI studies simulated with 275, 385, 500 and 750 Bq/voxel in the myocardium, whose short-axis (SA) slices were segmented with ImageJ by the threshold default, OTSU, and k-means Plugin Toolkit methods (k=2, k=3). To analyze the most appropriate segmentation method, the signal-to-noise ratio (SNR) for each short-axis (SA) slice was calculated, in accordance with the slices obtained from the software Quantitative Perfusion Single Photon Emission Computed Tomography® (QPS®) and by manual segmentation using ImageJ. To analyze the SNR with ImageJ and QPS® segmentation methods in the same simulated study, and to compare with the same segmentation method in different simulated studies, the Friedman and Kruskal-Wallis tests were applied. Results and discussion: The method k-means with k=3 is the most suitable method for the segmentation of the LV, regardless of the administered activity. Conclusion: This study may contribute to the clinical implementation of open-source based segmentation methods of the LV in MPI, according to the activity in the myocardium.info:eu-repo/semantics/publishedVersio

Computer image registration techniques applied to nuclear medicine images

Modern medicine has been using imaging as a fundamental tool in a wide range of applications. Consequently, the interest in automated registration of images from either the same or different modalities has increased. In this chapter, computer techniques of image registration are reviewed, and cover both their classification and the main steps involved. Moreover, the more common geometrical transforms, optimization and interpolation algorithms are described and discussed. The clinical applications examined emphases nuclear medicine