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Cardiovascular disease is one of the leading causes of death in Canada. Atherosclerosis is considered the primary cause for cardiovascular disease. Optical coherence tomography (OCT) provides a means to minimally invasive imaging and assessment of textural features of atherosclerotic plaque. However, detecting atherosclerotic plaque by visual inspection from Optical Coherence Tomography (OCT) images is usually difficult. Therefore we developed unsupervised segmentation algorithms to automatically detect atherosclerosis plaque from OCT images. We used three different clustering methods to identify atherosclerotic plaque automatically from OCT images. Our method involves data preprocessing of raw OCT images, feature selection and texture feature extraction using the Spatial Gray Level Dependence Matrix method (SGLDM), and the application of three different clustering techniques: K-means, Fuzzy C-means and Gustafson-Kessel algorithms to segment the plaque regions from OCT images and to map the cluster regions (background, vascular tissue, OCT degraded signal region and Atherosclerosis plaque) from the feature-space back to the original preprocessed OCT image. We validated our results by comparing our segmented OCT images with actual photographic images of vascular tissue with plaque.

Cardiovascular disease is one of the leading causes of death in Canada. Atherosclerosis is considered the primary cause for cardiovascular disease. Optical coherence tomography (OCT) provides a means to minimally invasive imaging and assessment of textural features of atherosclerotic plaque. However, detecting atherosclerotic plaque by visual inspection from Optical Coherence Tomography (OCT) images is usually difficult. Therefore we developed unsupervised segmentation algorithms to automatically detect atherosclerosis plaque from OCT images. We used three different clustering methods to identify atherosclerotic plaque automatically from OCT images. Our method involves data preprocessing of raw OCT images, feature selection and texture feature extraction using the Spatial Gray Level Dependence Matrix method (SGLDM), and the application of three different clustering techniques: K-means, Fuzzy C-means and Gustafson-Kessel algorithms to segment the plaque regions from OCT images and to map the cluster regions (background, vascular tissue, OCT degraded signal region and Atherosclerosis plaque) from the feature-space back to the original preprocessed OCT image. We validated our results by comparing our segmented OCT images with actual photographic images of vascular tissue with plaque.