Date of Award
7-1-2020
Thesis Type
phd
Document Type
Thesis (Restricted Access)
Divisions
eng
Department
Faculty of Engineering
Institution
Universiti Malaya
Abstract
Pediatricians often apply bone age assessment to measure the skeletal maturity of children and to predict the future height. These discrepancies are good indicators for diagnosing growth disorders. Normally, left hand skeletal is employed in this assessment. The low quality of ossification sites of carpals deteriorates the pediatrician’s visibility in inspecting the pertinent radiographic manifestations. This in turn affects the bone age assessment. Therefore, we have to enhance the quality before assessing them. Histogram equalization is one of the contrast enhancement techniques that suit this type of enhancement. Existing histogram equalizations, however, are confronting with problems in preserving the brightness and details as well as preventing the contrast from being over-enhanced or under-enhanced simultaneously. The comprehensive histogram equalization was proposed by considering all criteria of the desired histogram-equalized image to produce moderately contrast enhanced carpals’ ossification sites. Qualitative results show that the determining features of maturity stages have been emphasized in some of the Pareto optimized image. The improvement for Pareto optimized image by bi-histogram equalization is significant for five stages from stage D to stage H with improvement accuracy of 7.16%, 12.47%, 16.03%, 21.21% and 18.51%, respectively. Findings concluded that the Pareto optimized images able to improve the classifier accuracy that estimate the maturity stage of the carpal bones.
Note
Thesis (PhD) - Faculty of Engineering, Universiti Malaya, 2020.
Recommended Citation
Liang, Kim Meng, "An improved bone age assessment using advanced image processing and deep learning approach / Liang Kim Meng" (2020). Student Works (2020-2029). 270.
https://knova.um.edu.my/student_works_2020s/270