Stent segmentation in IOCT-TD images using gradient combination and mathematical morphology

Diego Armando Cardona Cardenas; Matheus Cardoso Moraes; Sérgio Shiguemi Furuie

doi:10.1117/12.2070515

28 January 2015 Stent segmentation in IOCT-TD images using gradient combination and mathematical morphology

Diego Armando Cardona Cardenas, Matheus Cardoso Moraes, Sérgio Shiguemi Furuie

Proceedings Volume 9287, 10th International Symposium on Medical Information Processing and Analysis; 928716 (2015) https://doi.org/10.1117/12.2070515
Event: Tenth International Symposium on Medical Information Processing and Analysis, 2014, Cartagena de Indias, Colombia

Abstract

In 2010, cardiovascular disease (CVD) caused 33% of the total deaths in Brazil. Modalities such as Intravascular Optical Coherent Tomography (IOCT) provides coronary in vivo for detecting and monitoring the progression of CVDs. Specifically, this type of modality is widely used in neo-intima post stent re-stenosis investigation. Computational methods applied to IOCT images can render objective structure information, such as areas, perimeters, etc., allowing more accurate diagnostics. However, the variety of methods in the literature applied in IOCT is still small compared to other related modalities. Therefore, we propose a stent segmentation approach based on extracted features by gradient operations, and Mathematical Morphology. The methodology can be summarized as following: the lumen is segmented and the contrast stretching is generated, both to be used as auxiliary information. Second, the edges of objects were obtained by gradient computation. Next, a stent extractor finds and select relevant stent information. Finally, an interpolation procedure followed by morphological operations ends the segmentation. To evaluate the method, 160 images from pig coronaries were segmented and compared to their gold standards, the images were acquired after 30, 90 and 180 days of stent implantation. The proposed approach present good accuracy of True Positive (TP(%)) = 96.51±5.10, False Positive (FP(%)) = 6.09±5.32 , False Negative (FN(%)) = 3.49±5.10. Conclusion, the good results and the low complexity encourage the use and continuous evolution of current approach. However, only images of IOCT-TD technology were evaluated; therefore, further investigations should adapt this approach to work with IOCT-FD technology as well.

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Diego Armando Cardona Cardenas, Matheus Cardoso Moraes, and Sérgio Shiguemi Furuie "Stent segmentation in IOCT-TD images using gradient combination and mathematical morphology", Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 928716 (28 January 2015); https://doi.org/10.1117/12.2070515

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KEYWORDS

Image segmentation

Image filtering

Convolution

Image processing

Optical coherence tomography

Chemical vapor deposition

Mathematical morphology

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