Author ORCID Identifier
https://orcid.org/0000-0002-0603-4979
Biosketch
Ananth Hari Ramakrishnan is a Assistant Professor - II in the department of School of Computing, SASTRA Deemed University, Thanjavur, India. He defended his PhD thesis in "Motion based analysis of ultrasound imaging for the study of Musculoskeletal Tissue Bio-Mechanics" in August 2024 from School of Computing, SASTRA. He has obtained his master’s degree in VLSI Design from SASTRA Deemed University, Thanjavur, India in the year 2017. He has obtained his bachelor’s degree in Electronics and Communication Engineering from National Engineering College, Kovilpatti, India in the year 2015. His research interest is on the general area of medical image processing with a special focus on processing musculoskeletal tissues. His Research work has been supported by the UK-India Education and Research Initiative (UKIERI) grant ‘Ultrasound based assessment of tissue biomechanics to enhance the clinical management of foot related pathologies’ (Project reference number: DST/INT/UK/P-145/2016).
Date of Award
31-8-2024
Document Type
Thesis
School
School of Computing
Programme
Ph.D.-Doctoral of Philosophy
First Advisor
Dr. R.Muthaiah
Second Advisor
Dr. K.Kannan
Keywords
Image Segmentation, Tendon, Bone, Segmentation Algorithm, Normalized Cross Correlation, Displacement Map, Optical Flow
Abstract
Ultrasound image analysis plays an important role in diagnosing musculoskeletal injuries and monitoring rehabilitation exercises. The first and foremost step in this analysis involves segmentation of region of interest from the ultrasound images. The segmentation of the musculoskeletal tissues from the ultrasound images is challenging due to the inherent drawback present in ultrasound like : (1) Poor image quality due to image corruption by speckle noise, shadows, and attenuation. (2) Dis-continuous boundaries due to orientation dependence during the acquisition of image. (3) Low contrast between nearby anatomical structures. Hence in order to overcome these drawbacks, there is a need for powerful segmentation algorithms for computerized segmentation of region of interest. The existing segmentation algorithms used for ultrasound images of musculoskeletal tissues are model based segmentation, machine learning and deep learning methods. Even though these methods are algorithmically distinctive they all rely on the same type of input information and try to distinguish the region of interest based on an analysis of signal intensity, texture, and shape features. However, in the case of ultrasound images such information might not be sufficient for boundaries. In such cases video analytics where the pattern of movement is also considered could enhance the analysis, however this approach has not as yet been tested in the case of medical imaging. Musculoskeletal tissues can be classified into two types, based on whether it is attached or detached from the surrounding tissues. When the musculoskeletal tissue is detached from the surrounding tissue, the tissue movement is simple and a simple pixel displacement information between a pair of frames is enough to segment the tissue based on movement information from the pair of frames. Example of such tissue is tendon. In the ultrasound video of tendon, tendon appears to move in the opposite direction to the surrounding tissues. This motion information is deployed for the segmentation of tendon from the surrounding tissues. The algorithm used for computing displacement between pair of frames are NCC and optical flow. The segmented output from the computerized segmentation is compared with the manual segmentation for calculation of accuracy. The algorithm having highest accuracy is considered as an accurate method for segmentation of tendon. When the musculoskeletal tissue is attached to the surrounding tissue, the movement information is complex and a simple displacement information from a pair of frames is not enough for tissue segmentation. Strain and average displacement are calculated from the multiple frames using which map of movement is created for these tissues. The map of movement from strain and average displacement helps in tissue segmentation. Example of such tissues is bone. The regions are tracked in the multiple frames using NCC and optical flow algorithm and map of movement is created from the motion information from these multiple frames. From the map of movement bone is having the highest movement compared to the surrounding tissues. This motion information is deployed for the segmentation of the bone from the surrounding tissues. The computerized segmented output are compared with the manually segmented output for determination of accuracy. The algorithm having highest accuracy is considered as an accurate method for segmentation of bone. From the segmentation of tendon and bone, algorithm having the highest accuracy is optical flow algorithm.
Recommended Citation
R, Ananth Hari Mr., "Motion Based Analysis of Ultrasound Imaging for the Study of Musculoskeletal Tissue Bio Mechanics" (2024). Theses and Dissertations. 3.
https://knowledgeconnect.sastra.edu/theses/3
Graphical Abstract