ArticleProceedings of SPIE--the International Society for Optical Engineering2011
Automatic Tissue Classification for High-resolution Breast CT Images Based on Bilateral Filtering.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19.Journal of applied statistics · 2023Article
- Fully automated breast segmentation on spiral breast computed tomography images.Journal of applied clinical medical physics · 2022Article
- Selection of Filtering and Image Texture Analysis in the Radiographic Images Processing of Horses' Incisor Teeth Affected by the EOTRH Syndrome.Sensors (Basel, Switzerland) · 2022Article
- An Automated Glowworm Swarm Optimization with an Inception-Based Deep Convolutional Neural Network for COVID-19 Diagnosis and Classification.Healthcare (Basel, Switzerland) · 2022Article
- Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification.Pattern recognition letters · 2021Article
- Classification of Coronavirus (COVID-19) from X-ray and CT images using shrunken features.International journal of imaging systems and technology · 2021Article
- An unsupervised automatic segmentation algorithm for breast tissue classification of dedicated breast computed tomography images.Medical physics · 2018Article
- A Patch-based CBCT Scatter Artifact Correction Using Prior CT.Proceedings of SPIE--the International Society for Optical Engineering · 2017Article
- Dosimetry in x-ray-based breast imaging.Physics in medicine and biology · 2016Article
- Breast tomosynthesis with monochromatic beams: a feasibility study using Monte Carlo simulations.Physics in medicine and biology · 2014Article
- Extracting Cardiac Myofiber Orientations from High Frequency Ultrasound Images.Proceedings of SPIE--the International Society for Optical Engineering · 2013Article
- A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications.Medical physics · 2013Review
- Cupping artifact correction and automated classification for high-resolution dedicated breast CT images.Medical physics · 2012Article
- Characterization of the homogeneous tissue mixture approximation in breast imaging dosimetry.Medical physics · 2012Article
- A multiscale and multiblock fuzzy C-means classification method for brain MR images.Medical physics · 2011Article
- A MR Brain Classification Method Based on Multiscale and Multiblock Fuzzy C-means.International Conference on Bioinformatics and Biomedical Engineering : [proceedings]. International Conference on Bioinformatics and Biomedical Engineering · 2011Article
- Multiscale segmentation of the skull in MR images for MRI-based attenuation correction of combined MR/PET.Journal of the American Medical Informatics Association : JAMIAArticle
- Power-law Beta Calculations for Realistic Mammography Images from Egs_cbct Simulation.Journal of medical physicsArticle
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3 authors.
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Abstract
Breast tissue classification can provide quantitative measurements of breast composition, density and tissue distribution for diagnosis and identification of high-risk patients. In this study, we present an automatic classification method to classify high-resolution dedicated breast CT images. The breast is classified into skin, fat and glandular tissue. First, we use a multiscale bilateral filter to reduce noise and at the same time keep edges on the images. As skin and glandular tissue have similar CT values in breast CT images, we use morphologic operations to get the mask of the skin based on information of its position. Second, we use a modified fuzzy C-mean classification method twice, one for the skin and the other for the fatty and glandular tissue. We compared our classified results with manually segmentation results and used Dice overlap ratios to evaluate our classification method. We also tested our method using added noise in the images. The overlap ratios for glandular tissue were above 94. 7% for data from five patients. Evaluation results showed that our method is robust and accurate.
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