Breast cancer is the third leading cause of death in women in the United States. In human breast tissue, adipose cells are infiltrated or replaced by cancer cells during the development of breast tumor. Therefore, an adipose map can be an indicator of identifying cancerous region. We developed an automated classification method to generate adipose map within human breast.
To facilitate the automated classification, we first mask the B-scans from OCT volumes by comparing the signal noise ratio with a threshold. Then, the image was divided into multiple blocks with a size of 30 pixels by 30 pixels. In each block, we extracted texture features such as local standard deviation, entropy, homogeneity, and coarseness. The features of each block were input to a probabilistic model, relevance vector machine (RVM), which was trained prior to the experiment, to classify tissue types. For each block within the B-scan, RVM identified the region with adipose tissue. We calculated the adipose ratio as the number of blocks identified as adipose over the total number of blocks within the B-scan.
We obtained OCT images from patients (n = 19) in Columbia medical center. We automatically generated the adipose maps from 24 B-scans including normal samples (n = 16) and cancerous samples (n = 8). We found the adipose regions show an isolated pattern that in cancerous tissue while a clustered pattern in normal tissue. Moreover, the adipose ratio (52.30 ± 29.42%) in normal tissue was higher than the that in cancerous tissue (12.41 ± 10.07%).
Yu Gan, Xinwen Yao, Ernest W. Chang, Syed A. Bin Amir, Hanina Hibshoosh, Sheldon Feldman, and Christine P. Hendon, "Automated adipose study for assessing cancerous human breast tissue using optical coherence tomography (Conference Presentation)," Proc. SPIE 10043, Diagnosis and Treatment of Diseases in the Breast and Reproductive System, 100430G (Presented at SPIE BiOS: January 29, 2017; Published: 19 April 2017); https://doi.org/10.1117/12.2253263.5370007604001.
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