Technical noteEstimating dynamic skin tension lines in vivo using 3D scans☆
Introduction
Langer’s line is a term used to define the direction within the human skin along which the skin has maximum tension. It is named after Langer, who punctured numerous holes at short distances from each other into the skin of a cadaver with an awl that had a circular-shaped tip, and noticed that the resultant punctures in the skin had ellipsoidal shapes. From this testing he determined “line directions” by smoothly connecting the longer axes of the ellipsoidal holes [1]. These lines, which are also termed as “cleavage lines”, show the directions of greatest skin tension. The surgical importance of Langer’s lines has been first noted by Kocher [2]. He advised that surgical incisions are carried out in the direction of Langer’s lines, because the scars would be least conspicuous and heal better. Since then, many surgeons have searched for the ideal guide to use for elective incisions, and there exist more than thirty differently named guidelines today [3].
One problem with Langer’s line has been that they represent lines of cleavage in cadavers and not lines of relaxed skin tension. Indeed, the facial musculature of a cadaver is often not relaxed, as some display a wide-eyed stare or wide-open mouth. Accordingly, surgeons have investigated less invasive methods to determine relaxed skin tension lines in-vivo, among them are Kraissl’s lines [2] and relaxed skin tension lines [4]. Kraissl’s line coincides with wrinkle lines, although not always, and tend to be perpendicular to the muscle action. Thus, Kraissl’s lines are found by studying the direction of underlying muscle fiber. Relaxed skin tension lines [4], [5] are the tension lines which follow the furrows formed when the skin is relaxed. In practice, they are derived from the act of pinching the skin and observing the furrows and ridges that are formed. Pinching parallel to the resting skin tension lines results in fewer and higher furrows than if it were done in the perpendicular direction. Although this method can provide personalized guidelines for elective incisions relatively easily, it may be subjective and may not be convenient, as it requires pinching.
We note that most of these lines are static in nature. That is, forces causing the tension on the skin are inherent in the skin or the body contents itself and not related to muscular action. However, surgeons and scientists agree that skin tension is dependent not only on the enclosed body content but also on joint movements. Similarly to the static tension lines, dynamic skin tension lines (DSTL) are defined as the maximum tension lines formed when the muscles are in action. To the best of our knowledge, the first work on the dynamic tension lines are by Bush et al. [6], who have measured the orientation of maximal strains on different location of the face with five different facial expressions. However, their experiments have been based on the excision of naevi from the face and neck, which is invasive and requires multiple subjects in order to be able to cover the entire face area.
The primary objective of this research is estimating personalized dynamic skin tension lines in vivo, in a non-invasive manner. We base our study on the kinematic analysis of point markers that are colored on the skin. By tracking the motion-induced displacement of point markers, we locally analyze the skin deformation and numerically compute the maximum tension directions. Then, finding DSTL is transformed to the problem of finding continuous, interpolating lines of the maximum tension directions. Smooth, continuous dynamic skin tension lines have been drawn on the skin surface by adopting interpolation and path-line integral over the marker meshes. Based on computational methods, our method is convenient to carry out, less prone to erroneous measurement, and repeatable. Our experiments have been carried out on, though not limited to, the skin on the legs of male subjects.
Section snippets
Data acquisition
A whole body 3-D range scanner [7] has been used to capture the shape of the subject as well as to track the location change of landmarks in the skin. Prior to the scan, red-colored circular landmarks, whose diameters are approximately 1 cm, have been regularly placed on the skin, approximately 2–3 cm apart. Fig. 1 shows the screenshots of range scans taken on two subjects holding four different postures. In order to analyze the skin deformation on the legs during knee bending action, we have
Marker extraction and labeling
We take the scanned mesh data as input and begin by separating the marker triangles from the rest of the skin mesh. Since the markers are drawn by colors that are clearly distinguishable (red) from that of the skin or of the underwear, we can easily identify marker triangles by using a color key. We locate each vertex in the color space as defined by the HSV (Hue-Saturation-Value) color model. We first map the color of each triangle vertex to the color space defined by the HSV the hexagonal
Triangle-based strain computation
We have devised a triangle based strain analysis method, by embedding a set of virtual strain gages on each triangle. Plain strain components located on the three dimensional surface can also be obtained by a conventional finite element method (FEM) [10], [11]. However, if we consider large deformation we should take care of nonlinear terms as well as subject-specific material properties of the skin. Moreover, the simulation requires very small time step, implying long computation time. Thus,
Extraction of DSTL
Our goal is now to compute smooth, continuous lines interpolating the maximal strain directions on each triangle. Note the task of DSTL extraction is related to but slightly different from tensor visualization [13], given with this specific goal of obtaining continuous lines on the skin surface. Assuming that the strain directions do not change abruptly between neighboring triangles, we adopt a vector-field based method to compute the smooth, continuous dynamic skin tension lines on the skin
Results
We have tested our method on three young males of different physical characteristics (thick, standard, and thin). In Fig. 4, we show the results of strain analysis and DSTL that we obtained by applying the proposed method on a thin male subject. Fig. 6 shows the maximal strain tension and dynamic tension lines we obtained for these subjects at 30 (left column), 45 (middle column), and 60 (right column) degrees of knee flexion. The degree of maximal strain tension is encoded as a colormap. The
Discussion
Since our algorithm is the first one tailored for the dynamic skin tension lines, it is difficult to perform any fair comparisons. Moreover, most of the known lines require invasive experiments involving incisions on the skin, which is beyond the scope of this study. Nevertheless, comparing DSTL with other known lines may give some insights on the characteristics of DSTL. Here we provide visual comparisons with Langer’s lines and Kraissle’s lines. RSTLs have been excluded from the comparison
Conclusion and limitations
We have demonstrated in this work how laser scanning technology can be used to measure the dynamic skin tension lines, i.e. the direction of skin tension caused by muscle actions such as knee flexion. By tracking the motion-induced displacement of marker points on the skin surface, the three components of the orthogonal strain tensor and the directions of maximum strain were computed for three representative male samples. The developed triangle-based strain analysis and the combined method of
References (13)
Karl Langer and his lines
Br J. Plast. Surg.
(1978)- et al.
A simplified model of scar contraction
Journal of Biomechanics
(2008) The selection of appropriate lines for elective surgical incisions
Plastic and Reconstructive Surgery
(1951)- et al.
Langers lines: to use or not to use
Plastic and Reconstructive Surgery
(1999) Relaxed skin tension lines (RSTL) versus other skin lines
Plastic and Reconstructive Surgery
(1984)Relaxed skin tension lines
Dermatologic Clinics
(1989)
Cited by (0)
- ☆
This work has been supported by Specific & Fundamental Research Program (No. 2009-0083874) of Korean National Research Foundation. The first author has been partly supported by the Imagerie et Robotique Mdicale et Chirurgical (IRMC) program and by the project SHARED (No. 10-CHEX-014-01) funded by French National Research Agency.