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Improved interactive medical image segmentation using Enhanced Intelligent Scissors (EIS)

TitleImproved interactive medical image segmentation using Enhanced Intelligent Scissors (EIS)
Publication TypeConference Paper
Year of Publication2008
AuthorsMishra, A., A. Wong, W. Zhang, D. A. Clausi, and P. Fieguth
Conference Name30th Annual Conference of the IEEE Engineering in Medicine and Biology Society
Date Published08/2008
Conference LocationVancouver, British Columbia, Canada
KeywordsAlgorithms, Artificial Intelligence, Automated, Computer Simulation, Computer-Assisted, Diagnostic Imaging, Humans, image enhancement, image interpretation, Information Storage and Retrieval, Markov Chains, Models, Pattern Recognition, Reproducibility of Results, Statistical, Visible Human Projects
Abstract

A novel interactive approach called Enhanced Intelligent Scissors (EIS) is presented for segmenting regions of interest in medical images. The proposed interactive medical image segmentation algorithm addresses the issues associated with segmenting medical images and allows for fast, robust, and flexible segmentation without requiring accurate manual tracing. A robust complex wavelet phase-based representation is used as an external local cost to address issues associated with contrast non-uniformities and noise typically found in medical images. The boundary extraction problem is formulated as a Hidden Markov Model (HMM) and the novel approach to the second-order Viterbi algorithm with state pruning is used to find the optimal boundary in a robust and efficient manner based on the extracted external and internal local costs, thus handling much inexact user boundary definitions than existing methods. Experimental results using MR and CT images show that the proposed algorithm achieves accurate segmentation in medical images without the need for accurate boundary definition as per existing Intelligent Scissors methods. Furthermore, usability testing indicate that the proposed algorithm requires significantly less user interaction than Intelligent Scissors.

DOI10.1109/IEMBS.2008.4649855