Strip steel surface defect detection is a critical step in the production field of the steel industry and a vital guarantee to improve the quality of strip steel production. However, due to the poor contrast of the strip steel surface defect images, the diversity of defect types, scales, texture structures, and the irregular distribution of defects, it is difficult to achieve rapid and accurate detection of strip steel surface defects with the existing methods. In this paper, a rapid detection network for strip steel surface defects based on deformable convolution and attention mechanism (DCAM-Net) is proposed. Firstly, we introduce the Limited Contrast Adaptive Histogram Equalization (CLAHE) as a data augmentation method to improve the contrast of the defect image and highlight the defect feature on the strip steel surface images. Secondly, we propose a novel Enhanced Deformation-feature Extraction block (EDE-block) for various complex and irregularly distributed strip steel defects. By fusing deformable convolution, the receptive field of the defect feature extraction network is expanded to capture complete and comprehensive defect texture features. Finally, we introduce the Coordination Attention module (CA) to replace the backbone network’s Spatial Pyramid Pooling structure (SPP), which further factorizes the pooling operation and effectively improves the network’s ability to locate defects. The experimental results on the NEU-DET dataset showed that the mean Average Precision (mAP@IoU=0.5) of the proposed algorithm is 82.6%, which is 7.3% higher than the baseline network, and the detection speed is up to 100.2 FPS, which effectively improves the detection efficiency of surface defects of strip steel.

The classification of benign and malignant based on ultrasound images is of great value because breast cancer is an enormous threat to womens health worldwide. Although both texture and morphological features are crucial representations of ultrasound breast tumor images, their straightforward combination brings little effect for improving the classification of benign and malignant since high-dimensional texture features are too aggressive so that drown out the effect of low-dimensional morphological features. For that, an efficient texture and morphological feature combing method is proposed to improve the classification of benign and malignant. Firstly, both texture (i.e., local binary patterns (LBP), histogram of oriented gradients (HOG), and gray-level co-occurrence matrixes (GLCM)) and morphological (i.e., shape complexities) features of breast ultrasound images are extracted. Secondly, a support vector machine (SVM) classifier working on texture features is trained, and a naive Bayes (NB) classifier acting on morphological features is designed, in order to exert the discriminative power of texture features and morphological features, respectively. Thirdly, the classification scores of the two classifiers (i.e., SVM and NB) are weighted fused to obtain the final classification result. The low-dimensional nonparameterized NB classifier is effectively control the parameter complexity of the entire classification system combine with the high-dimensional parametric SVM classifier. Consequently, texture and morphological features are efficiently combined. Comprehensive experimental analyses are presented, and the proposed method obtains a 91.11 accuracy, a 94.34 sensitivity, and an 86.49 specificity, which outperforms many related benign and malignant breast tumor classification methods.

In strip steel production, detecting surface defects is crucial for ensuring product quality and optimizing production line efficiency. However, detecting defects is complicated by the variations in size, complex structures, and the wide range of defect morphologies present in strip steel. To tackle these challenges, this paper proposes a strip steel surface defect detection network via adaptive focusing features (AFF-Net). Firstly, an adaptive focusing feature block (AFF-Block) is proposed, which applies the “Diffusion-Aggregation” thought. This block repositions and adaptively assigns weights to defect features, guiding the network to focus on defect features and more effectively capture defects’ spatial and morphological changes. Subsequently, a focused feature pyramid network (Foc-FPN) is proposed to enhance the network’s adaptability to complex defects through multi-scale focusing fusion. This innovative structure adaptively balances the semantic gap of defect features at different scales and alleviates the abstraction feature overload. The proposed algorithm achieved a mean Average Precision (mAP@IoU=0.5) of 83.5% on the public NEU-DET dataset for strip steel surface defects, surpassing the baseline network by 8.2%. Compared to existing models, this detection method strengthens the connection between defect characteristics and more effectively detects irregularly distributed defects in complex strip steel surface images.

Optical coherence tomography (OCT) is an emerging noninvasive imaging technique, which is based on low-coherence interferometry. OCT images suffer from speckle noise, which reduces image contrast. A shrinkage filter based on wave atoms transform is proposed for speckle reduction in OCT images. Wave atoms transform is a new multiscale geometric analysis tool that offers sparser expansion and better representation for images containing oscillatory patterns and textures than other traditional transforms, such as wavelet and curvelet transforms. Cycle spinning-based technology is introduced to avoid visual artifacts, such as Gibbs-like phenomenon, and to develop a translation invariant wave atoms denoising scheme. The speckle suppression degree in the denoised images is controlled by an adjustable parameter that determines the threshold in the wave atoms domain. The experimental results show that the proposed method can effectively remove the speckle noise and improve the OCT image quality. The signal-to-noise ratio, contrast-to-noise ratio, average equivalent number of looks, and cross-correlation (XCOR) values are obtained, and the results are also compared with the wavelet and curvelet thresholding techniques.

Prostate cancer is one of the most common cancers in men. Early detection of prostate cancer is the key to successful treatment. Ultrasound imaging is one of the most suitable methods for the early detection of prostate cancer. Although ultrasound images can show cancer lesions, subjective interpretation is not accurate. Therefore, this paper proposes a transrectal ultrasound image analysis method, aiming at characterizing prostate tissue through image processing to evaluate the possibility of malignant tumours. Firstly, the input image is preprocessed by optical density conversion. Then, local binarization and Gaussian Markov random fields are used to extract texture features, and the linear combination is performed. Finally, the fused texture features are provided to SVM classifier for classification. The method has been applied to data set of 342 transrectal ultrasound images obtained from hospitals with an accuracy of 70.93%, sensitivity of 70.00%, and specificity of 71.74%. The experimental results show that it is possible to distinguish cancerous tissues from noncancerous tissues to some extent.