Delineation of Coronary Arteries Using Localized Deformable Model and Statistical Thresholding of Vesselness Measure

Abstract

Coronary artery diseases are considered one of the major causes of mortality and morbidity in industrial countries. High rate of these life threatening diseases demands a timely diagnosis for their proper treatment. Medical imaging plays a vital role in capturing the internal organs of the human body to carry out necessary diagnosis and investigation procedure. Computed tomography angiography (CTA) is considered one of the most reliable imaging modality due to its high spatial resolution and invasive procedure. As compared to conventional angiography, CTA results in a stack of images that provides a more clear visibility of coronary arteries and presence of significant stenosis. Analyzing and examining such a volumetric data produced by CTA is a tedious and time consuming task for radiologists when it comes to a large number of patients. Here arises the need for computer-assisted diagnostic techniques and efficient algorithms that may help clinical experts to track the coronary arteries in volumetric datasets of heart in an automatic fashion. This dissertation aims to develop an automated framework for robustly delineating the coronary arteries from contrast enhanced CTA data efficiently. Various studies have been proposed in the literature addressing the problem of coronary delineation in either automatic or semi-automatic manner. However, it is observed that no single segmentation method fits to all types of images due to the diverse anatomical knowledge of the internal body organs. Most of the existing segmentation methods make use of model based approaches that may include region growing and active contour models, both of which requires initial single or multiple seeds to grow thus the performance of these methods may partially rely on the provision of starting points. Region growing methods normally perform segmentation by recruiting voxels in an increment fashion on the basis of some predefined condition. However, they often require user involvement to provide seeds and also due to the deviations in image intensities and noise, these methods may produce holes and results in over-segmentation. Apart from region growing methods, the most commonly used method for vessel segmentation is based on active contour also famous as snakes, which act as an energy minimization function. In snakes, the initially drawn contour is deformed in the presence of constraint and image forces that pull it towards the contours of the object and also the internal forces that repel the deformation. The model is deformed iteratively until the energy associated with the given contour is minimized such that it estimates the true shape of the given object. Broadly, edge-based and region-based are two variations of active contour model. Edge-based snake model relies on the edge information to deform the contour and became popular in its early stage. However, the model often fails where edges are not defined clearly such as medical images. Moreover, edge-based models rely on the initial placement of the contour near the object and also they do not take into account the topological changes that usually occur in case of vessels. On the contrary, region-based models perform delineation more robustly by computing the region based statistics globally. Chan-Vese algorithm is the pioneer algorithm that gives the concept of deforming contour by using the regional information in the presence of weak edges and unclear gradient. Also, it does not depend upon the initial placement of the curve. The global mean intensities of inside and outside region of the contour is modeled to achieve the final segmentation under the hypothesis of intensity homogeneity for gray scale. The shortcoming of CV model lies in its inefficiency to deal with smoothed intensities and intensity inhomogeneity that usually exists in medical image data. To overcome these limitations, the concept of energy localization has been introduced that utilizes the local information and at the same time incorporates the advantages of region-based methods. In this method regional statistics are measured locally which enhances the segmentation performance in the presence of varying intensities. However, scale selection shows another difficulty in these methods. Moreover, due to the slight differences in intensities as exhibited by coronary arteries and their neighboring voxels, the localized energy model produces leakages and may fail to produce an exact and complete segmentation till the distal ends of the arteries. To overcome the shortcomings discussed above, this dissertation presents an automatic and an efficient framework that performs the segmentation of coronary arteries accurately and robustly from given CTA data. The first phase of the proposed framework deals with detecting the coronary seed points automatically by computing the local geometric features of the vessels. Whereas, in the second phase, the detected seed points are fed into the localized statistical active contour model which is directed to evolve iteratively in two directions under the directions of the thresholded vesselness to grab all the potential coronary components. Moreover, an algorithm for mask updation has been proposed that keeps on updating its neighborhood at the end of each iteration to grab the coronary components which may appear far away. The efficiency of the proposed method has been validated on publically available CTA datasets provided by Rotterdam framework for lumen segmentation. Moreover, the proposed method has been compared with previous methods of coronary arteries segmentation and superior performance of the proposed method has been shown visually. Additionally, statistical comparison of the proposed method is also provided with the help of performance measures including Precision, Recall, Jaccard Index (JI), and F-measure.