A survey of fuzzy rule-based image segmentation techniques

This paper describes the various fuzzy rule based techniques for image segmentation. Fuzzy rule based segmentation techniques can incorporate domain expert knowledge and manipulate numerical as well as linguistic data. They are also capable of drawing partial inference using fuzzy IF-THEN rules. For these reasons they have been extensively applied in medical imaging. But these rules are application domain specific and it is very difficult to define the rules either manually or automatically so that the segementation can be achieved successfully

Human treelike tubular structure segmentation: A comprehensive review and future perspectives

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed

Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.Comment: 30 pages, 19 figures, submitted to CBM journa

Acute asthma and recovered airway tree geometry modeling and CFD simulation

This study focuses primarily on the development of modeling approaches for the reconstruction of lung airway tree and arterial vessel geometry models which will assist practical clinical studies. Anatomically-precise geometric models of human airways and arterial vessels play a critical role in the analysis of air and blood flows in human bodies. The generic geometric modeling methods become invalid when the model consists of both trachea and bronchioles or very small vessels. This thesis presents a new region-based method to reconstruct the entire airway tree and carotid vessels from point clouds obtained from CT or MR images. A novel layer-by-layer searching algorithm has been developed to recognize the branches of the airway tree and arterial vessels from the entire point clouds. Instead of applying a uniform accuracy on all branches regardless of the number of available points, the surface patches on each branch are constructed adaptively based on the number of available elemental points, which leads to the elimination of distortions occurring at small bronchi and vessels. Acute asthma is a serious disease of the respiratory system. To understand the difference in geometry and airflow patterns between acute asthma affected and recovered airway trees, a comparison study has been conducted in this research. Two computational models of the airway tree up to six generations deep were reconstructed from computed tomography (CT) scans from a single patient. The first scan was taken one day after an acute asthma episode and the second scan was taken thirty days later when the patient had recovered. The reconstructed models were used to investigate the effects of acute asthma on realistic airway geometry, airflow patterns, pressure drops, and the implications for targeted drug delivery. Comparisons in the geometry found that in general the right side of the airway is larger in diameter than the left side. The recovery of the airway was most significant in the severely asthma affected regions. Additionally the right airway branches exhibited greater dilation after recovery in comparison with the left airway especially from the fifth generation onwards. It was also found that bifurcation angles do not vary significantly between the two models, however small changes were observed which may be caused by the physical scans of the patient being taken at different times. The inhalation effort to overcome airway resistance in the asthma affected model was twice as high as that for the recovered model. Local flow patterns showed that the changes in the airway had significant influence on flow patterns. This was especially true in the region where the airway narrowing was most severe