Recent improvements in artificial intelligence and computer vision make it possible to automatically detect abnormalities in medical images. Skin lesions are one broad class of them. There are types of lesions that cause skin cancer, again with several types. Melanoma is one of the deadliest types of skin cancer. Its early diagnosis is at utmost importance. The treatments are greatly aided with artificial intelligence by the quick and precise diagnosis of these conditions. The identification and delineation of boundaries inside skin lesions have shown promise when using the basic image processing approaches for edge detection. Further enhancements regarding edge detections are possible. In this paper, the use of fractional differentiation for improved edge detection is explored on the application of skin lesion detection. A framework based on fractional differential filters for edge detection in skin lesion images is proposed that can improve automatic detection rate of malignant melanoma. The derived images are used to enhance the input images. Obtained images then undergo a classification process based on deep learning. A well-studied dataset of HAM10000 is used in the experiments. The system achieves 81.04% accuracy with EfficientNet model using the proposed fractional derivative based enhancements whereas accuracies are around 77.94% when using original images. In almost all the experiments, the enhanced images improved the accuracy. The results show that the proposed method improves the recognition performance.

Photoacoustic tomography (PAT) is a powerful imaging modality for visualizing tissue physiology and exogenous contrast agents. However, PAT faces challenges in visualizing deep-seated vascular structures due to light scattering, absorption, and reduced signal intensity with depth. Optical coherence tomography angiography (OCTA) offers high-contrast visualization of vasculature networks, yet its imaging depth is limited to a millimeter scale. Herein, we propose OCPA-Net, a novel unsupervised deep learning method that utilizes the rich vascular feature of OCTA to enhance PAT images. Trained on unpaired OCTA and PAT images, OCPA-Net incorporates a vessel-aware attention module to enhance deep-seated vessel details captured from OCTA. It leverages a domain-adversarial loss function to enforce structural consistency and a novel identity invariant loss to mitigate excessive image content generation. We validate the structural fidelity of OCPA-Net on simulation experiments, and then demonstrate its vascular enhancement performance on in vivo imaging experiments of tumor-bearing mice and contrast-enhanced pregnant mice. The results show the promise of our method for comprehensive vessel-related image analysis in preclinical research applications.

OBJECTIVE: To evaluate the added value of contrast-enhanced ultrasound (CEUS) on top of breast conventional imaging for predicting the upgrading of ductal carcinoma in situ (DCIS) to invasive cancer after surgery.
METHODS: This retrospective study enrolled 140 biopsy-proven DCIS lesions in 138 patients and divided them into two groups based on postoperative histopathology: non-upgrade and upgrade groups. Conventional ultrasound (US), mammography (MMG), CEUS and clinicopathological (CL) features were reviewed and compared between the two groups. The predictive performance of different models (with and without CEUS features) for histologic upgrade were compared to calculate the added value of CEUS.
RESULTS: Fifty-nine (42.1 %) lesions were histologically upgraded to invasive cancer after surgery. By logistic regression analyses, we found that high-grade DCIS at biopsy (P=0.004), ultrasonographic lesion size > 20 mm (P=0.007), mass-like lesion on US (P=0.030), the presence of suspicious calcification on MMG (P=0.014), the presence of perfusion defect (P=0.005) and the area under TIC>1021.34 ml (P<0.001) on CEUS were six independent factors predicting concomitant invasive components after surgery. The CL+US+MMG model made with the four predictors in the clinicopathologic, US and MMG categories yielded an area under the receiver operating curve (AUROC) value of 0.759 (95 % CI: 0.680-0.828) in predicting histological upgrade. The combination model built by adding the two CEUS predictors to the CL+US+MMG model showed higher predictive efficacy than the CL+US+MMG model (P=0.018), as the AUROC value was improved to 0.861 (95 % CI: 0.793-0.914).
CONCLUSIONS: The addition of contrast-enhanced ultrasound to breast conventional imaging could improve the preoperative prediction of an upgrade to invasive cancer from CNB -proven DCIS lesions.

In order to solve the problem of image quality and morphological characteristics of primary underglaze brown decorative pattern extraction, this paper proposes a method of primary underglaze brown decorative pattern extraction based on the coupling of single scale gamma correction and gray sharpening. The single-scale gamma correction is combined with the gray sharpening method. The single-scale gamma correction improves the contrast and brightness of the image by nonlinear transformation, but may lead to the loss of image detail. Gray sharpening can enhance the high frequency component and improve the clarity of the image, but it will introduce noise. Combining these two technologies can compensate for their shortcomings. The experimental results show that this method can improve the efficiency of last element underglaze brown decorative pattern extraction by enhancing the image retention detail and reducing the influence of noise. The experimental results showed that F1Score, Miou(%), Recall, Precision and Accuracy(%) were 0.92745, 0.82253, 0.97942, 0.92458 and 0.92745, respectively.

There are many high dam hubs in the world, and the regular inspection of high dams is a critical task for ensuring their safe operation. Traditional manual inspection methods pose challenges related to the complexity of the on-site environment, the heavy inspection workload, and the difficulty in manually observing inspection points, which often result in low efficiency and errors related to the influence of subjective factors. Therefore, the introduction of intelligent inspection technology in this context is urgently necessary. With the development of UAVs, computer vision, artificial intelligence, and other technologies, the intelligent inspection of high dams based on visual perception has become possible, and related research has received extensive attention. This article summarizes the contents of high dam safety inspections and reviews recent studies on visual perception techniques in the context of intelligent inspections. First, this article categorizes image enhancement methods into those based on histogram equalization, Retinex, and deep learning. Representative methods and their characteristics are elaborated for each category, and the associated development trends are analyzed. Second, this article systematically enumerates the principal achievements of defect and obstacle perception methods, focusing on those based on traditional image processing and machine learning approaches, and outlines the main techniques and characteristics. Additionally, this article analyzes the principal methods for damage quantification based on visual perception. Finally, the major issues related to applying visual perception techniques for the intelligent safety inspection of high dams are summarized and future research directions are proposed.

In response to the analysis of the functional status of forearm blood vessels, this paper fully considers the orientation of the vascular skeleton and the geometric characteristics of blood vessels and proposes a blood vessel width calculation algorithm based on the radius estimation of the tangent circle (RETC) in forearm near-infrared images. First, the initial infrared image obtained by the infrared camera is preprocessed by image cropping, contrast stretching, denoising, enhancement, and initial segmentation. Second, the Zhang-Suen refinement algorithm is used to extract the vascular skeleton. Third, the Canny edge detection method is used to perform vascular edge detection. Finally, a RETC algorithm is developed to calculate the vessel width. This paper evaluates the accuracy of the proposed RETC algorithm, and experimental results show that the mean absolute error between the vessel width obtained by our algorithm and the reference vessel width is as low as 0.36, with a variance of only 0.10, which can be significantly reduced compared to traditional calculation measurements.

BACKGROUND: High-fidelity cardiac magnetic resonance (MR) imaging plays a pivotal role in the surveillance of congenital heart disease (CHD) and aortopathy.
OBJECTIVE: We aimed to evaluate the quality and accuracy of free breathing, ECG-gated noncontrast three-dimensional (3D) balanced steady-state free precession (bSSFP) in cases of CHDs and aortopathies using contrast-enhanced 3D bSSFP as a reference. We also used one of our routinely used non-ECG-gated 2D-single-shot (SSh) bSSFP sequence as an adjunct to noncontrast 3D bSSFP.
METHODS: Institutional review board approval was obtained to perform a systematic retrospective analysis of image quality and vascular measurements. Patients with CHD and aortopathy, who were undergoing clinically indicated contrast-enhanced 3D bSSFP, were prospectively identified to also undergo additional noncontrast 3D bSSFP and 2D SSh bSSFP imaging as part of a clinical quality improvement initiative aimed at reducing the use of contrast when feasible. Two readers, blinded to each other\'s evaluations, graded image quality on a 5-point Likert scale and performed vascular measurements in separate sessions for both 3D bSSFP images. They also reported the visibility of various mediastinal great vessels on 2D SSh bSSFP images. Raw agreement, the weighted kappa statistic, and intra-class correlation coefficients (ICCs) were computed to assess the consistency and agreement between the two readers. Comparative analysis of noncontrast and contrast-enhanced 3D bSSFP imaging was performed in adult and pediatric patients using a two-sided paired t-test and Bland-Altman analysis. A P-value < 0.05 was considered significant for all inference testing.
RESULTS: A total of 29 patients (17 males, median age 20.3 years, interquartile range (IQR) 12.5, age range 7-39 years), including 11 pediatric patients under the age of 18 years (6 males, median age 14.5 years, IQR 4.0, age range 7-17 years), underwent retrospective analysis. The overall image quality score for contrast-enhanced 3D bSSFP was significantly higher (P < 0.0001) than that of noncontrast 3D bSSFP for both all subjects (4.4 ± 0.2, range 4.0-4.9 vs 3.7 ± 0.4, range 3.1-4.7) and only pediatric subjects (4.3 ± 0.3, range 4.0-4.9 vs 3.6 ± 0.5, range 3.1-4.4). By combining noncontrast 3D bSSFP and 2D bSSFP, reader 1 and reader 2 rated 423 and 420 vessels diagnostic, respectively, in a total of 435 vessel segments. All landmarks showed similar mean vessel diameters without significant differences between noncontrast and contrast-enhanced 3D bSSFP MR angiography (r = 0.99, bias -0.31 mm, 95% limits of agreement -2.04 mm to 1.43 mm).
CONCLUSIONS: Although contrast-enhanced images had better overall image quality, an imaging protocol consisting of noncontrast 2D SSh bSSFP and 3D bSSFP whole-chest images provides diagnostically adequate image quality, and accurate vascular measurements, comparable to free-breathing contrast-enhanced 3D bSSFP in both children and adults with CHD and aortopathies.

Texture and color enhancement imaging (TXI) may improve the visibility of gastric tumors and allow their early detection. However, few reports have examined the utility of TXI. Between June 2021 and October 2022, 56 gastric tumors in 51 patients undergoing endoscopic submucosal dissection at Fukuchiyama City Hospital were evaluated preoperatively using conventional white light imaging (WLI), narrow-band imaging (NBI), and TXI modes 1 and 2. The color differences of the tumors and surrounding mucosae were evaluated using the CIE 1976 L*a*b color space, Additionally, the visibility scores were scaled. Of the 56 gastric tumors, 45 were early gastric cancers, and 11 were adenomas. Overall, the color difference in TXI mode 1 was considerably higher compared to WLI (16.36 ± 7.05 vs. 10.84 ± 4.05; p < 0.01). Moreover, the color difference in early gastric cancers was considerably higher in TXI mode 1 compared to WLI, whereas no significant difference was found in adenomas. The visibility score in TXI mode 1 was the highest, and it was significantly higher compared to WLI. Regarding adenomas, the visibility score in TXI mode 1 was also significantly higher compared to that in WLI. TXI may provide improved gastric tumor visibility.

BACKGROUND: The quality of low-light endoscopic images involves applications in medical disciplines such as physiology and anatomy for the identification and judgement of tissue structures. Due to the use of point light sources and the constraints of narrow physiological structures, medical endoscopic images display uneven brightness, low contrast, and a lack of texture information, presenting diagnostic challenges for physicians.
METHODS: In this paper, a nonlinear brightness enhancement and denoising network based on Retinex theory is designed to improve the brightness and details of low-light endoscopic images. The nonlinear luminance enhancement module uses higher-order curvilinear functions to improve overall brightness; the dual-attention denoising module captures detailed features of anatomical structures; and the color loss function mitigates color distortion.
RESULTS: Experimental results on the Endo4IE dataset demonstrate that the proposed method outperforms existing state-of-the-art methods in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). The PSNR is 27.2202, SSIM is 0.8342, and the LPIPS is 0.1492. It provides a method to enhance image quality in clinical diagnosis and treatment.
CONCLUSIONS: It offers an efficient method to enhance images captured by endoscopes and offers valuable insights into intricate human physiological structures, which can effectively assist clinical diagnosis and treatment.

In positron emission tomography (PET) and X-ray computed tomography (CT), reducing radiation dose can cause significant degradation in image quality. For image quality enhancement in low-dose PET and CT, we propose a novel theoretical adversarial and variational deep neural network (DNN) framework relying on expectation maximization (EM) based learning, termed adversarial EM (AdvEM). AdvEM proposes an encoder-decoder architecture with a multiscale latent space, and generalized-Gaussian models enabling datum-specific robust statistical modeling in latent space and image space. The model robustness is further enhanced by including adversarial learning in the training protocol. Unlike typical variational-DNN learning, AdvEM proposes latent-space sampling from the posterior distribution, and uses a Metropolis-Hastings scheme. Unlike existing schemes for PET or CT image enhancement which train using pairs of low-dose images with their corresponding normal-dose versions, we propose a semi-supervised AdvEM (ssAdvEM) framework that enables learning using a small number of normal-dose images. AdvEM and ssAdvEM enable per-pixel uncertainty estimates for their outputs. Empirical analyses on real-world PET and CT data involving many baselines, out-of-distribution data, and ablation studies show the benefits of the proposed framework.