The　assessment　of　images　of　the　coronary　artery　system　plays　a　crucial　part　in　the　diagnosis　and　treatment　of　cardiovascular　diseases　(CVD).　Forward-looking　intravascular　ultrasound　(FL-IVUS)　has　a　distinct　advantage　in　assessing　CVD　due　to　its　superior　resolution　and　imaging　capability,　especially　in　severe　calcification　scenarios.　The　demarcation　of　the　lumen　and　media-adventitia,　as　well　as　the　identification　of　calcified　tissue　information,　constitute　the　initial　steps　in　assessing　of　CVD　such　as　atherosclerosis　using　FL-IVUS　images.　In　this　research,　we　introduced　a　novel　approach　for　automated　lumen　segmentation　and　identification　of　calcified　tissue　in　FL-IVUS　images.　The　proposed　method　utilizes　superpixel　segmentation　and　fuzzy　C-means　clustering　(FCM)　to　identify　regions　that　potentially　correspond　to　lumina.　Furthermore,　connected　component　labeling　and　active　contour　methods　are　employed　to　refine　the　contours　of　lumina.　To　handle　the　distinctive　depth　information　found　in　FLIVUS　images,　ellipse　fitting　and　region　detectors　are　applied　to　identify　areas　with　calcified　tissue.　In　our　dataset　consisting　of　43　FL-IVUS　images,　this　method　achieved　mean　values　for　Jaccard　measure,　Dice　coefficient,　Hausdorff　distance,　and　percentage　area　difference　at　0.952　f　0.016,　0.975　f　0.008,　0.296　f　0.186,　and　0.019　f　0.010,　respectively.　Furthermore,　when　compared　with　traditional　segmentation　approaches,　the　proposed　approach　yields　higher　images　quality.　The　test　results　demonstrate　the　effectiveness　of　this　innovative　automated　segmentation　technique　for　detecting　the　lumina　and　calcified　tissue　in　FL-IVUS　images.