| Download | - View accepted manuscript: Added soft tissue contrast using signal attenuation and the fractal dimension for optical coherence tomography images of porcine arterial tissue (PDF, 1.2 MiB)
|
|---|
| DOI | Resolve DOI: https://doi.org/10.1088/0031-9155/55/8/013 |
|---|
| Author | Search for: Flueraru, C.1; Search for: Popescu, D. P.2; Search for: Mao, Y.1; Search for: Chang, S.1; Search for: Sowa, M. G.2 |
|---|
| Affiliation | - National Research Council of Canada. NRC Institute for Microstructural Sciences
- National Research Council of Canada. NRC Institute for Biodiagnostics
|
|---|
| Format | Text, Article |
|---|
| Abstract | Optical coherence tomography (OCT) images of left-descending coronary tissues harvested from three porcine specimens were acquired with a home-build swept-source OCT setup. Despite the fact that OCT is capable of acquiring high resolution circumferential images of vessels, many distinct histological features of a vessel have comparable optical properties leading to poor contrast in OCT images. Two classification methods were tested in this report for the purpose of enhancing contrast between soft-tissue components of porcine coronary vessels. One method involved analyzing the attenuation of the OCT signal as a function of light penetration into the tissue. We demonstrated that by analyzing the signal attenuation in this manner we were able to differentiate two media sub-layers with different orientations of the smooth muscle cells. The other classification method used in our study was fractal analysis. Fractal analysis was implemented in a box-counting (fractal dimension) image-processing code and was used as a tool to differentiate and quantify variations in tissue texture at various locations in the OCT images. The calculated average fractal dimensions had different values in distinct regions of interest (ROI) within the imaged coronary samples. When compared to the results obtained by using the attenuation of the OCT signal, the method of fractal analysis demonstrated better classification potential for distinguishing amongst the tissue ROI. |
|---|
| Publication date | 2010-04-01 |
|---|
| In | |
|---|
| Language | English |
|---|
| Peer reviewed | Yes |
|---|
| NPARC number | 17653075 |
|---|
| Export citation | Export as RIS |
|---|
| Report a correction | Report a correction (opens in a new tab) |
|---|
| Record identifier | 261c8bbb-6090-4792-a1c8-351ec1e4b554 |
|---|
| Record created | 2011-04-03 |
|---|
| Record modified | 2020-04-17 |
|---|
