Developing an Automatic Vision based Abdominal Muscle Extraction and Analysis Method from Ultrasonography

Analyzing abdominal muscles and measuring useful associated morphological features is important in many clinical researches. While ultrasonography is a non-invasive reliable tool for such tasks, it may cause the experimenter dependent subjective diagnosis thus a computer vision based automatic muscle detector/analyzer is much needed in this area. In this paper, we propose such an automatic vision based method using a series of image processing algorithms. The novelty of our method is to extract internal oblique muscle from abdominal ultrasonographic image which was excluded in previous study due to their irregular features. Previously, we used Mask searching method to restore vague part of abdominal image but the third layer of muscle (transverse abdominis) was not clearly extracted because of the image distortion in the process. In order to analyze muscle morphometric features like the thickness, the second layer (internal oblique muscle) should also be extracted correctly thus we develop a new muscle extraction process that includes extracting the second layer by using unsharp masking. Extraction of transverse abdominis is also improved by developing a new data structure Both-Map in candidate search process. In that process, we save the morphological features of initially extracted muscle area and later such information is used to restore the fascia area with various image processing techniques to extract target muscle accurately. The effectiveness of the proposed method is verified by the practical diagnostic field of rehabilitation in the extraction accuracy and the magnitude of the muscle thickness measurement error that lies within 0.02cm in more than 60% of cases used in the experiment.