Analyzing multimodality tomographic images and associated regions of interest with MIDAS

Wai-Hon Tsui; Henry Rusinek; Peter Van Gelder; Sergey Lebedev

doi:10.1117/12.431059

3 July 2001 Analyzing multimodality tomographic images and associated regions of interest with MIDAS

Wai-Hon Tsui, Henry Rusinek, Peter Van Gelder, Sergey Lebedev

Proceedings Volume 4322, Medical Imaging 2001: Image Processing; (2001) https://doi.org/10.1117/12.431059
Event: Medical Imaging 2001, 2001, San Diego, CA, United States

Abstract

This paper outlines the design and features incorporated in a software package for analyzing multi-modality tomographic images. The package MIDAS has been evolving for the past 15 years and is in wide use by researchers at New York University School of Medicine and a number of collaborating research sites. It was written in the C language and runs on Sun workstations and Intel PCs under the Solaris operating system. A unique strength of the MIDAS package lies in its ability to generate, manipulate and analyze a practically unlimited number of regions of interest (ROIs). These regions are automatically saved in an efficient data structure and linked to associated images. A wide selection of set theoretical (e.g. union, xor, difference), geometrical (e.g. move, rotate) and morphological (grow, peel) operators can be applied to an arbitrary selection of ROIs. ROIs are constructed as a result of image segmentation algorithms incorporated in MIDAS; they also can be drawn interactively. These ROI editing operations can be applied in either 2D or 3D mode. ROI statistics generated by MIDAS include means, standard deviations, centroids and histograms. Other image manipulation tools incorporated in MIDAS are multimodality and within modality coregistration methods (including landmark matching, surface fitting and Woods' correlation methods) and image reformatting methods (using nearest-neighbor, tri-linear or sinc interpolation). Applications of MIDAS include: (1) neuroanatomy research: marking anatomical structures in one orientation, reformatting marks to another orientation; (2) tissue volume measurements: brain structures (PET, MRI, CT), lung nodules (low dose CT), breast density (MRI); (3) analysis of functional (SPECT, PET) experiments by overlaying corresponding structural scans; (4) longitudinal studies: regional measurement of atrophy.

Citation Download Citation

Wai-Hon Tsui, Henry Rusinek, Peter Van Gelder, and Sergey Lebedev "Analyzing multimodality tomographic images and associated regions of interest with MIDAS", Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); https://doi.org/10.1117/12.431059

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