TY - BOOK
T1 - Automated classification system for detection and prediction of osteoarthritis in human knee joints
AU - Woloszynski, Tomasz
PY - 2011
Y1 - 2011
N2 - [Truncated abstract] The development of an automated classification system for detection and prediction of knee osteoarthritis (OA) is of great interest to the medical community. The system, once developed, would aid or replace human experts in the assessment of risk and severity of knee OA and other chronic and progressive joint diseases. Also, the system would provide inexpensive and reliable means for patient monitoring and diagnosis and hence it would be a valuable tool in the evaluation of drug treatment effects against knee OA. To date, a few attempts to develop such a system have been reported in the literature. However, the systems developed cannot detect the disease in its earliest stage and they are sensitive to knee imaging conditions. Therefore, there is a growing need for the development of an accurate and robust system for detection and prediction of knee OA. This thesis is divided into three parts. The first part presents the development (Chapter 2) and evaluation (Chapters 2 and 3) of a new method for measuring distances between trabecular bone (TB) texture regions selected on knee radiographs. The method developed, called a signature dissimilarity measure (SDM), quantifies texture roughness and orientation at predefined scales that can be adjusted to trabecular image sizes at which OA changes are most prominent. Unlike other methods, the SDM method is invariant to in-plane rotation and predefined scales. To evaluate the method developed, data sets of TB texture images from healthy and OA knees and from knees with non-progressive and progressive OA were constructed...
AB - [Truncated abstract] The development of an automated classification system for detection and prediction of knee osteoarthritis (OA) is of great interest to the medical community. The system, once developed, would aid or replace human experts in the assessment of risk and severity of knee OA and other chronic and progressive joint diseases. Also, the system would provide inexpensive and reliable means for patient monitoring and diagnosis and hence it would be a valuable tool in the evaluation of drug treatment effects against knee OA. To date, a few attempts to develop such a system have been reported in the literature. However, the systems developed cannot detect the disease in its earliest stage and they are sensitive to knee imaging conditions. Therefore, there is a growing need for the development of an accurate and robust system for detection and prediction of knee OA. This thesis is divided into three parts. The first part presents the development (Chapter 2) and evaluation (Chapters 2 and 3) of a new method for measuring distances between trabecular bone (TB) texture regions selected on knee radiographs. The method developed, called a signature dissimilarity measure (SDM), quantifies texture roughness and orientation at predefined scales that can be adjusted to trabecular image sizes at which OA changes are most prominent. Unlike other methods, the SDM method is invariant to in-plane rotation and predefined scales. To evaluate the method developed, data sets of TB texture images from healthy and OA knees and from knees with non-progressive and progressive OA were constructed...
KW - Osteoarthritis
KW - Bone
KW - Texture classification
M3 - Doctoral Thesis
ER -