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.
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