This paper presents a light-weight process for 3D reconstruction and measurement of chronic wounds using a commonly available smartphone as an image capturing device. The first stage of our measurement pipeline comprises the creation of a dense 3D point cloud using structure-from-motion (SfM). Furthermore, the wound area is segmented from the surrounding skin using dynamic thresholding in CIELAB color space and a surface is estimated to simulate the missing skin in the wound area. Together with a mesh reconstruction of the wound, the skin surface and the segmented wound is used to calculate the wound dimensions, i.e., its length, surface area and volume. We evaluate the presented pipeline using three wound phantoms, representing different stages in healing, and compare the subsequently scanned and measured wound dimensions with manually measured ones.

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The workflow in modern hospitals entails that the medical treatment of a patient is distributed between several physicians and nurses. This leads to intensive cooperation, which takes place under particular time pressure and requires efficient conveyance of relevant patient-related medical data to colleagues. This requirement is difficult to achieve with traditional data representation approaches. In this paper, we introduce a novel concept of anatomically integrated in-place visualization designed to engage with cooperative tasks on a neurosurgical ward by using a virtual patient’s body as spatial representation of visually encoded abstract medical data. Based on the findings of our field studies, we provide a set of formal requirements and procedures for this kind of visual encoding. Moreover, we implemented a prototype on a mobile device that supports the diagnosis of spinal disc herniation and has been evaluated by 10 neurosurgeons. The physicians have assessed the proposed concept as beneficial, especially emphasizing the advantages of the anatomical integration such as intuitiveness and a better data availability due to providing all information at a glance. Particularly, four of nine respondents have stressed solely benefits of the concept, other four have mentioned benefits with some limitations and only one person has seen no benefits.

Periodic, wave-like modifications of 2D shape contours are often applied to convey quantitative data via images. However, to the best of our knowledge, there has been no in-depth investigation of the perceptual uniformity and legibility of these kind of approaches. In this paper, we design and perform a user study to evaluate the perception of periodic contour modifications related to their geometry and colour. Based on the study results, we statistically derive a perceptual model, which demonstrates a mainly linear stimulus-to-perception relationship for geometric and colour amplitude and a close-to-quadratic relationship for the respective frequencies, with a rather negligible dependency on the waveform. Furthermore, analyzing the distribution of perceived magnitudes and the overlapping of the respective 50% confidence intervals, we extract distinguishable, visually equidistant quantization levels for each contour-related visual variable. Moreover, we give first insights into the perceptual dependency between amplitude and frequency, and propose a scheme for transferring our model to glyphs with different size, which preserves the distinguishability and the visual equidistance. This work is seen as a first step towards a comprehensive understanding of the perception of periodic contour modifications in image-based visualizations.