Interpretation of medical infrared images is complex due to thermal noise, absence of texture, and small temperature
differences in pathological zones. Acute inflammatory response is a characteristic symptom of some knee injuries like
anterior cruciate ligament sprains, muscle or tendons strains, and meniscus tear. Whereas artificial coloring of the
original grey level images may allow to visually assess the extent inflammation in the area, their automated segmentation
remains a challenging problem. This paper presents a hybrid segmentation algorithm to evaluate the extent of
inflammation after knee injury, in terms of temperature variations and surface shape. It is based on the intersection of
rapid color segmentation and homogeneous region segmentation, to which a Laplacian of a Gaussian filter is applied.
While rapid color segmentation enables to properly detect the observed core of swollen area, homogeneous region
segmentation identifies possible inflammation zones, combining homogeneous grey level and hue area segmentation.
The hybrid segmentation algorithm compares the potential inflammation regions partially detected by each method to
identify overlapping areas. Noise filtering and edge segmentation are then applied to common zones in order to segment
the swelling surfaces of the injury. Experimental results on images of a patient with anterior cruciate ligament sprain
show the improved performance of the hybrid algorithm with respect to its separated components. The main contribution
of this work is a meaningful automatic segmentation of abnormal skin temperature variations on infrared thermography
images of knee injury swelling.