This paper presents a novel approach to automatic classifying and identifying of tree leaves using image segmentation fusion. With the development of mobile devices and remote access, automatic plant identification in images taken in natural scenes has received much attention. Image segmentation plays a key role in most plant identification methods, especially in complex background images. Wher...

Computed tomography (CT) is the modality of choice for imaging the lungs in vivo. Sub-millimeter isotropic images of the lungs can be obtained within seconds, allowing the detection of small lesions and detailed analysis of disease processes. The high resolution of thoracic CT and the high prevalence of lung diseases require a high degree of automation in the analysis pipeline. The automated se...

Regional quantitative analysis of airway morphological abnormalities is of great interest in lung disease investigation. Considering that pulmonary lobes are relatively independent functional unit, we develop and test a novel and efficient computerized scheme in this study to automatically and robustly classify the airways into different categories in terms of pulmonary lobe. Given an airway tr...

Assessing airway wall surfaces and the lumen from high resolution computed tomography (CT) scans are of great importance for diagnosing pulmonary diseases. However, accurately determining inner and outer airway wall surfaces of a complete 3-D tree structure can be quite challenging because of its complex nature. In this paper, we introduce a computational framework to accurately quantify airway...

Modeling the sequential information of image sequences has been a vital step various vision tasks and convolutional long short-term memory (ConvLSTM) demonstrated its superb performance in such spatiotemporal problems. Nevertheless, hierarchical data structures significant amount (e.g., human body parts vessel/airway tree biomedical images) cannot be properly modeled by models. Thus, ConvLSTM i...

In this paper, we present a semi-automatic region growing algorithm to segment the intrathoracic airway tree from 3-d CT images. A common problem with region growing is leakage. In order to limit leakage, our method bounds the segmentation using cylinders of adaptive orientation and dimensions. The leaks are detected based on anatomical information of the airways and an algorithm to avoid them ...

We introduce a self-assessed region growing technique capable of producing airway segmentations with reasonable quality. The main advantages of our technique are its robustness against leakage, and the absence of any training stages. Our method can not be considered fully automatic as it requires manual seeding of the trachea region, although there exists a variety of techniques to circumvent t...