Automated Thigh Muscles Segmentation using Hierarchical Multi-Atlas and FRFCM Methods in CT Scan Images

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Molaie Malihe; AGHAEIZADEH ZOROOFI REZA

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING Year:2019 | Volume:13 | Issue:3 Page(s): 269-282

Download Full-Text

Persian Verion

View:

315

Download:

Cites:

Information Journal Paper

Title

Automated Thigh Muscles Segmentation using Hierarchical Multi-Atlas and FRFCM Methods in CT Scan Images

Author(s)

Molaie Malihe | AGHAEIZADEH ZOROOFI REZA | Issue Writer Certificate

Keywords

Concurrent segmentationQ1

Thigh musclesQ1

Multi-atlas methodQ1

FRFCMQ1

CT scan imagesQ1

Abstract

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.

Cites

No record.

References

No record.

Cite

APA: Copy

Molaie, Malihe, & AGHAEIZADEH ZOROOFI, REZA. (2019). Automated Thigh Muscles Segmentation using Hierarchical Multi-Atlas and FRFCM Methods in CT Scan Images. IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING, 13(3 ), 269-282. SID. https://sid.ir/paper/81705/en

Vancouver: Copy

Molaie Malihe, AGHAEIZADEH ZOROOFI REZA. Automated Thigh Muscles Segmentation using Hierarchical Multi-Atlas and FRFCM Methods in CT Scan Images. IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING[Internet]. 2019;13(3 ):269-282. Available from: https://sid.ir/paper/81705/en

IEEE: Copy

Malihe Molaie, and REZA AGHAEIZADEH ZOROOFI, “Automated Thigh Muscles Segmentation using Hierarchical Multi-Atlas and FRFCM Methods in CT Scan Images,” IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING, vol. 13, no. 3 , pp. 269–282, 2019, [Online]. Available: https://sid.ir/paper/81705/en

Related Journal Papers