Document Type : Full Research Paper


1 Ph.D. Student, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

2 Professor, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran


Quantifying and modeling of the skeletal muscles can lead to an easier investigation of muscle diseases, specific mobility problems, and required simulations for the relevant surgeries. To this end, medical images should be segmented, firstly. In this research, thigh muscles segmentation is performed in CT images, since these muscles play a critical role in walking and balancing the body. To this aim, a multi-atlas method is used which is an improvement of the hierarchical multi-atlas method in the previous work. In this method, the muscles region is extracted automatically from the other tissues using FRFCM (Fast and Robust Fuzzy C-Means Clustering) method after the preprocessing stage. This muscle binary mask and the improved mask are used in the multi-atlas method for individual muscle segmentation. The proposed method is implemented using 20 CT data sets consisting of 12 female and 8 male subjects. The results show a less consumed computational time than the hierarchical multi-atlas method. The average computational time required for the muscles segmentation using the proposed method is 24 seconds and for the hierarchical multi-atlas method is 71 seconds per one slice of each case. Therefore, the proposed method reduces the implementation time by a rough factor of three.  The means of the Dice similarity coefficient for the proposed method with improved muscle mask and for the hierarchical multi-atlas method are 86.58±7.69 and 83.07±8.26, respectively.  The means of the precision and sensitivity for our method are 89.78±9.6 and 84.63±9.25, and for the hierarchical multi-atlas method are 88.85±12.04 and 78.04±10.88. Consequently, this method has better results based on the Dice similarity coefficient, precision, and sensitivity metrics.


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