Motion tracking in infrared imaging for quantitative medical diagnostic applications

Abstract

In medical applications, infrared (IR) thermography is used to detect and examine the thermal signature of skin abnormalities by quantitatively analyzing skin temperature in steady state conditions or its evolution over time, captured in an image sequence. However, during the image acquisition period, the involuntary movements of the patient are unavoidable, and such movements will undermine the accuracy of temperature measurement for any particular location on the skin. In this study, a tracking approach using a template-based algorithm is proposed, to follow the involuntary motion of the subject in the IR image sequence. The motion tacking will allow to associate a temperature evolution to each spatial location on the body while the body moves relative to the image frame. The affine transformation model is adopted to estimate the motion parameters of the template image. The Lucas-Kanade algorithm is applied to search for the optimized parameters of the affine transformation. A weighting mask is incorporated into the algorithm to ensure its tracking robustness. To evaluate the feasibility of the tracking approach, two sets of IR image sequences with random in-plane motion were tested in our experiments. A steady-state (no heating or cooling) IR image sequence in which the skin temperature is in equilibrium with the environment was considered first. The thermal recovery IR image sequence, acquired when the skin is recovering from 60-s cooling, was the second case analyzed. By proper selection of the template image along with template update, satisfactory tracking results were obtained for both IR image sequences. The achieved tracking accuracies are promising in terms of satisfying the demands imposed by clinical applications of IR thermography.

Keywords: Dynamic infrared imaging; Infrared thermography; Medical thermography; Motion tracking; Quantitative infrared imaging; Template-based algorithm.

Fig. 1

Schematic of subject's motion due to respiration and small involuntary movements of the body and limbs in the clinical IR imaging environment with the patient positioned in the exam chair.

Fig. 2

Sample infrared images illustrating the subject's in-plane motion in the acquired IR image sequence. The white arrows show the motion direction relative the previous frame (the adjacent frame on the left hand side), the rectangle is the template of known dimensions used for matching white light and infrared images, and the crosshair indicates a particular pixel location in the image frame, originally positioned in the center of the rectangular template. The changes of the location of the rectangular template relative to the crosshair are caused by involuntary motion of the subject.

Fig. 3

(a) The Merlin midwave infrared camera and the IR image acquisition system; (b) Two paper adhesive markers: the square one serves as the tracking template and the round one represents the simulated lesion for error analysis; (c) IR image of the adhesive markers shown on the monitor connected to the IR camera.

Fig. 4

(a) Weighting mask of the template image for the steady-state IR image sequence, (b) weighting mask of the template image for the thermal recovery IR image sequence (created after the cooling is applied), (c) the appearance of the template images after applying the weighting masks shown in (a) for steady state analysis and (d) for the thermal recovery shown in (b).

Fig. 5

(a) Adhesive markers and the segmented outline of the simulated lesion (red circle); (b) Red cross: the centroid location of the simulated lesion. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 6

Registration of the simulated lesion from the steady-state IR image (a) to the thermal recovery IR image (b) in Experiment B. The color change from (a) to (b) within the rectangular template is characteristic for the cooling process in dynamic IR imaging. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 7

Tracking results for Experiment A in a steady-state image sequence (circle – location of the centroid predicted by the tracking algorithm, cross – actual centroid location of the simulated lesion). The magnified view of the simulated lesion is displayed at the top right-hand corner of each image, the white arrows (right column) indicate the direction and the magnitude of the motion with respect to the previous frame (left column).

Fig. 8

Tracking error analysis for the steady-state image sequence in experiment A: the red line represents the frame-to-frame displacement (from frame i-1 to frame i) of the lesion centroid, and the blue bars represent the Euclidean distance between the predicted (circle center in Fig. 7) and actual (cross center in Fig. 7) location of the centroid. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 9

Tracking performance of Experiment B-1 for the first 23 frames in the thermal recovery IR image sequence (circle – centroid location predicted by the tracking algorithm, cross – actual centroid location of the simulated lesion).

Fig. 10

Tracking error analysis (Experiment B-1) of the thermal recovery image sequence after the removal of cooling: the red line represents the frame-to-frame displacement (from frame i-1 to frame i) of the simulated centroid. The blue bars represent the Euclidean distance between the predicted (circle in Fig. 9) and actual (cross in Fig. 9) location of lesion centroid. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 11

Tracking performance for Experiment B-2 with tracking duration of 90 s into the thermal recovery phase without template update: (a) four representative image frames illustrating the differences between centroid location predicted by the algorithm and the actual lesion centroid; (b) tracking errors and frame-to-frame displacement as a function of time.

Fig. 12

(a) Updated template image, frame 29, (b) updated weighting mask for frame 29 and (c) resulting temperature value for the updated template image after applying the weighting mask.

Fig. 13

Tracking performance of Experiment B-3 for 90 image frames with a template update carried out at frame 29: (a) Four representative frames showing the location differences between the predicted and actual lesion centroid and (b) the tracking errors at each frame along with the frame-to-frame displacement.