Since the results of existing intelligent algorithms of pulmonary nodule detection only predict the positions of nodules and cannot give semantical interpretations which are well known to doctors in clinical diagnosis, such as “lobulation”, “texture” and “spiculation”, a pulmonary nodule detection method with semantic feature score was proposed. Eight semantic features—subtlety, internal structure, lobulation, spiculation, margin, calcification, sphericity and texture were embedded into the Region Proposal Network (RPN) of Faster R-CNN, a new anchor box mechanism was designed, a fully connected network was added to realize the regression learning of semantic features, and the semantic scores were used as auxiliary information to realize the joint learning of pulmonary nodule detection and semantic prediction by training with Faster R-CNN. The proposed method was evaluated on the LIDC/IDRI dataset. Results show that the accuracy of pulmonary nodule localization is 91.2%, and the accuracy, sensitivity and specificity of benign and malignant classification are 81%, 91.2% and 70.8% respectively. On 8 semantic feature scores, the difference between doctors is 0.58±0.78 (mean absolute error±standard deviation), the proposed method achieves the difference of 0.62±1.03 with doctors, which is familiar to the former one. These results demonstrate that the modified network has good prediction accuracy and semantic feature prediction, and facilitates the understanding and clinical interpretations of machine prediction results for doctors.

In order to objectively and accurately evaluate the image fusion algorithms, an evaluation algorithm based on TV-L¹ (Total Variation regularization) structure and texture decomposition was proposed. According to the studies on human visual system, human's perception to image quality mainly comes from the underlying visual features of image, and structure features and texture features are the most important features of underlying visual feature of image. However, the existed image fusion quality evaluation algorithms ignore this fact and lead to inaccurate evaluation. To address this problem, a pair of source images and their corresponding fusion results were individually decomposed into structure and texture images with a two-level TV-L¹ decomposition. Then, According to the difference of image features between the structure and texture images, the similarity evaluation was carried out from the decomposed structure image and the texture image respectively, and the final evaluation score was obtained by integrating the scores at all levels. Based on the dataset with 30 images and 8 mainstream fusion algorithms, compared with the 11 existing objective evaluation indexes, the Borda counting method and Kendall coefficient were employed to verify the consistency of the proposed evaluation algorithm. Moreover, the consistency between the proposed objective evaluation index and the subjective evaluation is verified on the subjective evaluation image set.

The current image sharpness evaluation methods suffer from insufficient discrimination and monotonicity as well as the narrow applicable scope in the process of measurement. In order to solve the problems, an image sharpness evaluation method based on amplitude and phase of Quaternion Wavelet Transform (QWT) was proposed. The image was transformed into frequency domain from spatial domain by QWT, the amplitude and phase information of low frequency subband and high frequency subbands were got by further calculation of the QWT coefficients. After multiplication of the gradient of amplitude and corresponding directional phase, the proposed sharpness metrics were obtained by adding each direction's value together. The proposed sharpness metrics were conducted on the images with different content, degree of blur and degree of noise. Compared with the existing algorithms, the proposed algorithm has a fine monotonicity and discrimination for all kinds of images. The experimental results show that the proposed sharpness metrics not only overcome the shortcomings of the existing algorithms in the monotonicity and discrimination, but also can be applied to image processing.

In the process of remote sensing images fusion, the spectral distortion of fusion image is the main problem. To reduce distortion, an optimization image fusion method in combination with entropy component analysis transform was proposed. First, multi-band image was transformed to a small amount of bands by the entropy component analysis to reduce the spectral dimension. Projection transformation was finished from the perspective of entropy contribution so as to keep more information of source bands. Wavelet decomposition was done between the first entropy component and the high resolution image after histogram matching to get low frequency and high frequency subbands. For the fusion of low frequency subbands, Quantum-behaved Particle Swarm Optimization (QPSO) algorithm was applied to select the optimal weight coefficients. For the high frequency subbands, statistical feature and statistical model were used to perform fusion. The result of wavelet fusion was regarded as the first entropy principal component. The fusion image was obtained by wavelet and entropy component inverse transform. Entropy, cross entropy, standard deviation, grad, correlation coefficient and spectral distortion were selected as objective evaluation indexes. The experimental results show that the proposed method can enhance the spatial information and avoid spectral distortion.