Connected operators

Abstract

Abstract. In this article, we present the work towards improving the overall workflow of the Percutaneous Coronary Interventions (PCI) procedures by capacitating the imaging instruments to precisely monitor the steps of the procedure. In the long term, such capabilities can be used to optimize the image acquisition to reduce the amount of dose or contrast media employed during the procedure. We present the automatic VOIDD algorithm to detect the vessel of intervention which is going to be treated during the procedure by combining information from the vessel image with contrast agent injection and images acquired during guidewire tip navigation. Due to the robust guidewire tip segmentation method, this algorithm is also able to automatically detect the sequence corresponding to guidewire navigation. We present an evaluation methodology which characterizes the correctness of the guide wire tip detection and correct identification of the vessel navigated during the procedure. On a dataset of 2213 images from 8 sequences of 4 patients, VOIDD identifies vesselof-intervention with accuracy in the range of 88% or above and absence of tip with accuracy in range of 98% or above depending on the test case.

Partition and Inclusion Hierarchies of Images: A Comprehensive Survey

“…An exhaustive survey of filtering tools called connected operators was offered by Salembier and Wilkinson [52], focusing on their characteristics and techniques used to create them, and encompassing both the "classical" filtering approaches based on pixel-based representations as well as the approaches based on component trees. Approaches based on simplifying component trees with pruning and non-pruning strategies act on the connected components where the image is constant, bridging the gap between filtering and segmentation.…”

“…Basic image simplification or filtering [52] using an inclusion tree includes cutting branches from the leaves up to a region satisfying a desired criterion and collapsing all the removed regions into their closest surviving ancestor. Inclusion trees in literature are based on gray level or contrast between regions, and as such model dark and bright image structures.…”

“…A whole variety of connected operators can be realized by replacing the Max-tree with other component trees, and this fundamental extension to the framework of connected operators was studied in [52]. Recently, Xu and co-authors [85,139] have proposed defining the filtering in shape space, an image whose adjacency relation is given by the parental relations in its original Max-tree.…”

Retrieval of Remote Sensing Images with Pattern Spectra Descriptors

“…However, the range of this attribute is much smaller than that of the CNC attribute, so that the attribute values of interest are between 0 and 8 [6]. Attributes assigned to nodes can also be based on more complex notions, such as texture, motion, or even similarity to predefined shapes [57]. Many other attribute examples can be found in [49,51,58].…”

Meaningful disjoint level lines selection

“…Salembier and Wilkinson [16] give a detailed review of the tree-based filtering strategies. The works in [9,10,17,2,11,7,18,8] are closest to what we propose here.…”