| Summary: | 碩士 === 中原大學 === 電機工程研究所 === 98 === Segmentation of magnetic resonance (MR) images has numerous clinical applications: Being an auxiliary tool in diagnosis and treatment, making epidemiological statistics easier to carry on, being an important step in data analysis of all kinds of medical researches. However, most of the segmentation has been done manually or at most semi-automatically. The process is time- and energy-consuming and difficult to be standardized. Automatic segmentation algorithms of MR images for some of the body parts, such as the brain, have been developed with success. The muscular tissue is a major component of the human body, however, to our knowledge, no similar studies had been done on automatic muscle segmentation on MR images.
The goal of this study is to develop an automatic segmentation scheme to correctly assign different functional muscle groups on MR images of the human lower extremities. We speculated that the results could be used in increasing muscle-tissue-related researches, such as the monitoring of muscle volume change over the time for the victims of muscular dystrophy diseases and investigation of the use and the performance of different muscles in athletes of various sport types. The grouping and the surrounding anatomic structures are quite different for muscles located at different body parts. For convenience and to support another research of our labs, we chose to target at lower extremities, muscles from pelvis to ankle, as the object of the study. Since the MR signals for all muscles are more or less similar, instead of singling out an individual muscle, our study focused on automatic classification of functional muscle groups.
In the thesis, the lower extremities were first divided into several longitudinal anatomic segments based on the principles of proximity and anatomic similarity. Since the MR signals are quite different among bones, fascia, fat, and muscles, subcutaneous fat, ilium of the pelvis, femur, tibia, fibula, meniscus, and the muscular fascia are available as boundaries. Equipped by this knowledge, we applied mathematical morphological operations such as erosion, dilation, open, and close, and other image processing techniques such as regional growing and filling to designate muscle areas on an MR image as one of the eight muscle functional groups. The results of the automatic segmentation were then compared against the classification manually made by a physical therapist. We found that the average total absolute error (relative error) ± the standard deviation of our automatic segmentation is 1736.5 (13.5%) ± 1071.8 ml, with 490.2 (14.0%) ± 371.8 ml of the knee extensors as the largest absolute error and 41.3 (7.7%) ± 11.6 ml of the ankle flexors as the smallest. On the other hand, the largest relative error is hip flexors’ 242.8 (24.4%) ± 140.2 ml and the smallest the ankle extensors’ 153.1 (6.7%) ± 94.3 ml.
The study presents that the combined mathematical morphology and human anatomy knowledge approach successfully divided muscles of lower extremity MR images into meaningful functional groups without human intervention. In the future, the accuracy of this method could be further improved by more sophisticated revision such as MR-atlas registration. Applications on other body parts and tissues such as abdominal visceral fat are under investigation. We expect the results of this and related studies to be helpful in body-composition-related researches and perhaps also in clinical diagnosis and treatment.
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