This study aims at high-frequency ultrasound image quality assessment for computer-aided diagnosis of skin. In recent decades, high-frequency ultrasound imaging opened up new opportunities in dermatology, utilizing the most recent deep learning-based algorithms for automated image analysis. An individual dermatological examination contains either a single image, a couple of pictures, or an image series acquired during the probe movement. The estimated skin parameters might depend on the probe position, orientation, or acquisition setup. Consequently, the more images analyzed, the more precise the obtained measurements. Therefore, for the automated measurements, the best choice is to acquire the image series and then analyze its parameters statistically. However, besides the correctly received images, the resulting series contains plenty of non-informative data: Images with different artifacts, noise, or the images acquired for the time stamp when the ultrasound probe has no contact with the patient skin. All of them influence further analysis, leading to misclassification or incorrect image segmentation. Therefore, an automated image selection step is crucial. To meet this need, we collected and shared 17,425 high-frequency images of the facial skin from 516 measurements of 44 patients. Two experts annotated each image as correct or not. The proposed framework utilizes a deep convolutional neural network followed by a fuzzy reasoning system to assess the acquired data’s quality automatically. Different approaches to binary and multi-class image analysis, based on the VGG-16 model, were developed and compared. The best classification results reach $91.7\%$ accuracy for the first, and $82.3\%$ for the second analysis, respectively.

The paper presents the multistep methodology of bimodal Patient Specific Phantom (PSP) development. First, CT based abdominal digital model is designed. It serves as a source for designing organ moulds manufactured by means of a 3D-printer. The collagen based colloid fills the moulds yielding the organ casts. The PSP permits a bimodal navigation system to be developed that employs a realistic CT-based digital model and US imaging. Highly accurate results were achieved with mean Dice similarity coefficient value of 0.92 and Hausdorff distance 9.67 mm.

With the aging population of the developed countries, the diseases of affluence become common. One of the consequences of their complications are chronic wounds that affect more and more people. Because the healing of chronic wounds is a lengthy process, its monitoring should be as simple as possible, yet providing accurate and reliable documentation. In this paper, we present an image acquisition system and wound surface reconstruction method using several imaging modalities: color photography, thermal imaging, and depth perception. The proposed method is dedicated mainly to wounds located on the limbs where body curvature is large, and its influence on 2D results cannot be neglected. Our approach minimizes the extra effort taken by the medical staff to prepare the wound outline as it is still performed using 2D color photo and then mapped into the 3D space. The method was validated on 29 data sets containing two 3D point clouds from two depth imaging devices (depth camera and stereo camera) as well as 2D color photos and thermal maps. The approach was compared with expert delineations as well as other contemporary methods for wound surface reconstruction presented in the literature. Performed experiments and the obtained results show that the proposed method is statistically concordant with expert delineations performed in 3D.