A model based method for recognizing psoas major muscles in torso CT images

In aging societies, it is important to analyze age-related hypokinesia. A psoas major muscle has many important functional capabilities such as capacity of balance and posture control. These functions can be measured by its cross sectional area (CSA), volume, and thickness. However, these values are calculated manually in the clinical situation. The purpose of our study is to propose an automated recognition method of psoas major muscles in X-ray torso CT images. The proposed recognition process involves three steps: 1) determination of anatomical points such as the origin and insertion of the psoas major muscle, 2) generation of a shape model for the psoas major muscle, and 3) recognition of the psoas major muscles by use of the shape model. The model was built using quadratic function, and was fit to the anatomical center line of psoas major muscle. The shape model was generated using 20 CT cases and tested by 20 other CT cases. The applied database consisted of 12 male and 8 female cases from the ages of 40's to 80's. The average value of Jaccard similarity coefficient (JSC) values employed in the evaluation was 0.7. Our experimental results indicated that the proposed method was effective for a volumetric analysis and could be possible to be used for a quantitative measurement of psoas major muscles in CT images.