The Juxtapleural nodule regions are often missed in the result of lung segmentation algorithms. To tackle this problem, an identification method basing on the SIFT information and elliptic Fourier descriptor is proposed. Firstly, the SIFT information is used to locate the position of key points in the lung mask. Then with the help of distance relationship, the support borderlines of key points are calculated. Thirdly, the elliptic Fourier descriptor is introduced to describe a support line. Finally, an adaptive threshold is designed to decide whether the current support line is corresponding to an under-segmented region. Experiments on real CT images demonstrate that the proposed model provides an efficient way to perform under-segmented region identification task.
Lung juxtapleural nodule regions are often excluded from the extracted lung region by the intensity information-based methods. In order to solve this problem, an adaptive morphology template method is proposed. First of all, the SIFT information is used to extract some feature points in the borderline. Then, the Fourier descriptor is introduced to identify those juxtapleural nodule regions from all borderline sections. Finally, adaptive morphology templates are used to correct the recognized region. Through the experiments on real CT slices, perfect correction effect proves that the proposed model has a good power of re-correction for CT images.
Image salient region detection is widely used in many fields, such as automatic target detection, image retrieval, object recognition and image segmentation. Although there are many methods related to image salient region detection, improving its accuracy is still one of the hot research areas in image processing. In this paper, we propose a novel salient detection algorithm based on nonlocal filtering, which is mainly used in image denoising. The proposed algorithm stacks several similar blocks to form a three-dimensional matrix by image block matching, in which the filtered image by nonlocal filtering can better preserve image detail information than traditional Gaussian filtering. We calculate the Euclidean distance between the mean of the original image and the filtered image as the saliency map. The experimental results show that the proposed algorithm can accurately obtain the saliency map, especially, the edge of the saliency map image is much better than many existing algorithms.
In this paper, a method based on non-local saturation algorithm is proposed to avoid block and halo effect for single image dehazing with dark channel prior. First we convert original image from RGB color space into HSV color space with the idea of non-local method. Image saturation is weighted equally by the size of fixed window according to image resolution. Second we utilize the saturation to estimate the atmospheric light value and transmission rate. Then through the function of saturation and transmission, the haze-free image is obtained based on the atmospheric scattering model. Comparing the results of existing methods, our method can restore image color and enhance contrast. We guarantee the proposed method with quantitative and qualitative evaluation respectively. Experiments show the better visual effect with high efficiency.
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