Detecting landscapes such as rivers, mountains or forests in the complex scenes is a challenging problem in the field of infrared image, which has been applied widely in military and civilian. This paper aims to detect the river from infrared images, presenting an approach that combines the local binary pattern (LBP) and morphology to extract the river. The LBP descriptor is highly discriminative, computationally efficient and stable for monotonous gray level images. In this paper, we propose an improved LBP descriptor, which adopts a radius of 1.5 pixels and uses every neighbor pixel of center to acquire more information. Firstly, we preprocess the data and extract the feature by the improved LBP descriptor. Then a combination of the threshold processing, filtering and morphological operator is used to emphasize the feature result. Finally, through connected component analysis, the maximum connected component is focused to detect the river in the infrared image. The performance of algorithm is tested on a set of images. Areas of the extracted river and time cost are measured as well.
The accuracy and efficiency of the lung segmentation are significant to computer-aided detection/diagnosis (CAD/CADx) scheme for pulmonary nodules detection in chest computed tomography (CT) image. And morphology is widely utilized to characterize the shape of the object in lung segmentation. In this investigation, a multi-stages based approach which combines thresholding, connected component analysis and morphology is proposed to achieve a fast and precise lung segmentation. The presented framework consists of three stages: thorax extraction, lung segmentation and boundary refinement. A dataset of CT scans from different equipments and modalities is utilized to evaluate the proposed method. The average dice similarity coefficient (DSC) of the experiments is 0.97 and average time-consuming of each slice is 0.64s. The results demonstrate that the proposed method with multi-stages is an efficient and accurate method for lung segmentation.