Vessel tree reconstruction in thoracic CT scans with application to nodule detection

Abstract

Vessel tree reconstruction in volumetric data is a necessary prerequisite in various medical imaging applications. Specifically, when considering the application of automated lung nodule detection in thoracic computed tomography (CT) scans, vessel trees can be used to resolve local ambiguities based on global considerations and so improve the performance of nodule detection algorithms. In this study, a novel approach to vessel tree reconstruction and its application to nodule detection in thoracic CT scans was developed by using correlation-based enhancement filters and a fuzzy shape representation of the data. The proposed correlation-based enhancement filters depend on first-order partial derivatives and so are less sensitive to noise compared with Hessian-based filters. Additionally, multiple sets of eigenvalues are used so that a distinction between nodules and vessel junctions becomes possible. The proposed fuzzy shape representation is based on regulated morphological operations that are less sensitive to noise. Consequently, the vessel tree reconstruction algorithm can accommodate vessel bifurcation and discontinuities. A quantitative performance evaluation of the enhancement filters and of the vessel tree reconstruction algorithm was performed. Moreover, the proposed vessel tree reconstruction algorithm reduced the number of false positives generated by an existing nodule detection algorithm by 38%.

Automatic 3D pulmonary nodule detection in CT images: A survey

“…The radiologist may also suffer interference factors such as fatigue, Authors Computational technique(s) Choi and Choi [24], Santos et al [4], Chen et al [27] and Li and Doi [80] Hessian matrix based method El-Baz et al [22] and Le et al [81] Genetic algorithm template matching Cascio et al [26] Stable 3D mass-spring models Soltaninejad, Keshani and Tajeripour [28] k-Nearest Neighbors (k-NN) classifier and active contour Suiyuan and Junfeng [29] Thresholding Awai et al [82] Sieve filter Tanino et al [83] Variable n-quoit filter Riccardi et al [30] 3D fast radial transform Namin et al [32] and Murphy et al [84] Shape index Ozekes, Osman and Ucan [38] 3D template matching Ge et al [45] Adaptive weighted k-means clustering Yamada et al [85] and Kanazawa et al [86] Fuzzy clustering Mekada et al [51] Maximum distance inside a connected component Mao et al [87] Fragmentary window filtering Mendonça et al [88] Curvature tensor Paik et al [89] Statistical shape model Agam and Armato [90] Correlation-based enhancement filters Wang et al [25] and Armato III et al [91] Multiple gray-level thresholding Saita et al [92] 3D labeling method subjectivity of the analysis, images acquired with improper configuration of the equipment and noise. A detailed analysis of the LIDC-IDRI database can help us understand the difficulties encountered during this task.…”

Geodesic voting for the automatic extraction of tree structures. Methods and applications

“…While there are some studies dedicated to the segmentation of microglia structures, there are many studies dedicated to the extraction of vas-cular or airway trees. For a review of such methods see [3,4,5,6,7,8].…”

Detection of Microcalcification Clusters Using Hessian Matrix and Foveal Segmentation Method on Multiscale Analysis in Digital Mammograms

“…They used Hessian matrix for vessel structures enhancement combined with multiscale analysis. Yu and Zhao [16] and Agam et al [17] developed an algorithm to detect nodule structures on CT Images. They used Gaussian kernels of varying standard deviation for detecting local structures of images.…”