Transferable HMM probability matrices in multi‐orientation geometric medical volumes segmentation. (7th March 2019)

• Record Type: Journal Article
• Authors: AlZu'bi, Shadi; AlQatawneh, Sokyna; ElBes, Mohammad; Alsmirat, Mohammad
• Is Part Of: Concurrency and computation. Volume 32:Number 21(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-03-07
• Subjects: hidden Markov model -- image segmentation -- medical imaging -- transfer learning -- 3D volumes; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5214
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Acceptable error rate, low quality assessment, and time complexity are the major problems in image segmentation, which needed to be discovered. A variety of acceleration techniques have been applied and achieve real time results, but still limited in 3D. HMM is one of the best statistical techniques that played a significant rule recently. The problem associated with HMM is time complexity, which has been resolved using different accelerator. In this research, we propose a methodology for transferring HMM matrices from image to another skipping the training time for the rest of the 3D volume. One HMM train is generated and generalized to the whole volume. The concepts behind multi‐orientation geometrical segmentation has been employed here to improve the quality of HMM segmentation. Axial, saggital, and coronal orientations have been considered individually and together to achieve accurate segmentation results in less processing time and superior quality in the detection accuracy.