The possibility of solitary pulmonary nodules tending to lung cancer is very high in the middle and late stage. In order to detect the middle and late solitary pulmonary nodules, we present a new computer-aided diagnosis method based on the geometric features. The new algorithm can overcome the disadvantage of the traditional algorithm which can't eliminate the interference of vascular cross section. The proposed algorithm was implemented by multiple clustering of the extracted geometric features of region of interest (ROI) through K-means algorithm, including degree of slenderness, similar degree of circle, degree of compactness and discrete degree. The 232 lung CT images were selected from Lung Image Database Consortium (LIDC) database to do contrast experiment. Compared with the traditional algorithm, the detection rate of the new algorithm was 92.3%, and the error rate was 14.8%. At the same time, the detection rate of the traditional algorithm was only 83.9%, and the error rate was 78.2%. The results show that the proposed algorithm can mark the solitary pulmonary nodules more accurately and reduce the error rate due to precluding the disturbance of vessel section.
To address the high computational complexity of the Transformer in the segmentation of ultrasound thyroid nodules and the loss of image details or omission of key spatial information caused by traditional image sampling techniques when dealing with high-resolution, complex texture or uneven density two-dimensional ultrasound images, this paper proposes a thyroid nodule segmentation method that integrates the receiving weighted key-value (RWKV) architecture and spherical geometry feature (SGF) sampling technology. This method effectively captures the details of adjacent regions through two-dimensional offset prediction and pixel-level sampling position adjustment, achieving precise segmentation. Additionally, this study introduces a patch attention module (PAM) to optimize the decoder feature map using a regional cross-attention mechanism, enabling it to focus more precisely on the high-resolution features of the encoder. Experiments on the thyroid nodule segmentation dataset (TN3K) and the digital database for thyroid images (DDTI) show that the proposed method achieves dice similarity coefficients (DSC) of 87.24% and 80.79% respectively, outperforming existing models while maintaining a lower computational complexity. This approach may provide an efficient solution for the precise segmentation of thyroid nodules.