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Dental panoramic image analysis on mandibular bone for osteoporosis early detection
Suprijanto S.a, Juliastuti E.a, Diputra Y.b, Mayantasari M.b, Azhari A.c
a Instrumentation and Control Research Group, Indonesia
b Master Program of Instrumentation and Control, Faculty of Industrial Technology, Institut Teknologi Bandung, Indonesia
c Department of Dental Radiography, Faculty of Dentistry, Padjadjaran University, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]Osteoporosis is a degenerative disease characterized by low bone density and micro architectural deterioration of bone tissue with a consequent increase in bone fragility and decreasing bone mechanical force on supporting body normal activity. One of common technique used for measurement bone mass, bone mineral density or other aspect related bone structure is Dual Energy X-ray Absorptiometry (DXA). Previous researchers shown opportunity to utilize dental panoramic images for early detection and estimate the probability of having osteoporosis. However, a robust image quantitative is still challenge. In the paper, quantitative of dental panoramic is reported based on Gray Level Co-occurrence Matrix (GLCM). Feature extraction from GLCM will be use as an input for Support Vector Machine (SVM) algorithm to classify normal and osteoporosis. The classification result will validate using BMD data of 23 samples prepared by Dental Radiographs Department, where panoramic images are imaged from patient postmenopausal, with ages 52-73 year. Classification using SVM with kernel function multilayer perceptron for normal and osteoporosis showed that the best performance (using 9 training data and 14 test data) was 85,71% accuracy, 90,91% sensitivity, and 66,67% specificity. It’s best performance result is obtained by using contrast, correlation, energy, and homogeneity combination for SVM classification input. © 2013 IEEE.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Classification results,Co-occurance matrices,Dual energy x-ray absorptiometry,Gray level co occurrence matrix(GLCM),Multi layer perceptron,Osteoporosis,Panoramic radiograph,Support vector machine algorithm[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]dental panoramic radiograph,Grey Level Co-Occurance Matrix (GLCM),Osteoporosis,Support Vector Machine (SVM),textur analysis[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICA.2013.6734060[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]