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Automated segmentation of breast tissue and pectoral muscle in digital mammography
Rahmatika A.a, Handayani A.a, Setiawan A.W.a
a School of Electrical Engineering Informatics, Bandung Institute of Technology, Bandung, 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]© 2019 IEEE.Breast tissue and pectoral muscle segmentation are an important preliminary processing step in automated analysis of breast tumors from digital mammography. Without proper pectoral muscle exclusion, textural analysis of the breast tissue imagery would be hindered. This paper presents an automated segmentation of breast tissue and pectoral muscle in digital mammography based on simple histogram operation and k-means classification of the mammogram pixel intensity. We tested our algorithm in 25 digital medio-lateral oblique (MLO) mammograms with confirmed microcalcifications provided by the mini MIAS public database. Our results show that the proposed algorithm can successfully delineate the boundary of pectoral muscle next to the adjacent breast tissue. The best accuracy is achieved in mammograms of fatty-glandular dominant breast tissue and the worst accuracy is achieved in dense-glandular breast mammograms. Due to its simplicity and independence to mammogram intensity range, the algorithm may have potentials to be implemented in the pipeline of mammographic computer-Aided diagnosis to assist large-scale screening of breast cancer.[/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]Automated segmentation,Breast tissues,Digital mammography,K-means,Microcalcifications,Pectoral muscle segmentations,Pectoral muscles,Preliminary processing[/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]Breast tissue,K-means,Mammography,Pectoral muscle,Segmentation[/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]ACKNOWLEDGMENT The first author would like to thank the Studentship Body of Bandung Institute of Technology for scholarship support.[/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/ICAIIT.2019.8834455[/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]