Skip to Main content Skip to Navigation

Image processing for semantic analysis of the coronary interventions in cardiology

Abstract : Percutaneous coronary intervention (PCI) is performed using real-time radiographic imaging in an interventional suite. Modeling these ICP procedures to help the practitioner involves understanding the different phases of the ICP procedure, by the interventional machine, which can be used to optimize the X-ray dose and the contrast agent. One of the important tasks in achieving this goal is to segment different interventional tools into the flow of fluoroscopic images and to derive semantic information from them. The component tree, a powerful mathematical morphological tool, forms the basis of the proposed segmentation methods. We present this work in two parts: 1) the segmentation of the low-contrast empty catheter, and 2) the segmentation of the tip of the guide and the monitoring of the detection of the intervention vessel. We present a new multi-scale space-based segmentation method for detecting low-contrast objects such as an empty catheter. For the last part, we present the segmentation of the tip of the guide with filtering based on the component tree and propose an algorithm to semantically follow the segmented tip to determine the intervention vessel
Document type :
Complete list of metadata

Cited literature [197 references]  Display  Hide  Download
Contributor : ABES STAR :  Contact
Submitted on : Tuesday, January 15, 2019 - 12:24:14 PM
Last modification on : Saturday, January 15, 2022 - 3:56:55 AM
Long-term archiving on: : Tuesday, April 16, 2019 - 1:54:59 PM


Version validated by the jury (STAR)


  • HAL Id : tel-01981918, version 1


Ketan Bacchuwar. Image processing for semantic analysis of the coronary interventions in cardiology. Document and Text Processing. Université Paris-Est, 2018. English. ⟨NNT : 2018PESC1074⟩. ⟨tel-01981918⟩



Record views


Files downloads