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SFFS–SVM based prostate carcinoma diagnosis in DCE-MRI via ACM segmentation

  • Chuan-Yu ChangEmail author
  • Kathiravan Srinivasan
  • Hui-Ya Hu
  • Yuh-Shyan Tsai
  • Vishal Sharma
  • Punjal Agarwal
Article
  • 42 Downloads

Abstract

The prostate carcinoma is amongst the most commonly occurring cancers in Taiwanese males. Moreover, it is one of the chief reasons for cancer deaths among Taiwanese men, and early diagnosis of prostate cancer is vital for effective treatment. In this work, a diagnosis model for identifying the prostate carcinoma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is proposed. The urologists utilize the DCE-MRI as a support mechanism for better diagnosis of the carcinoma development in the prostate. Gadolinium is utilized as the contrast agent for the DCE-MRI data, and it was injected once and the time series data were captured at distinct time intervals of 0, 20, 60, and 100 s correspondingly. Primarily, after pre-processing the DCE-MRI information, the prostate data is segmented by employing the active contour model. Subsequently, 136 features are extracted from the segmented prostrate expanse of the DCE-MRI data, and the relative intensity change curve is computed. Afterward, Fisher’s discriminant ratio and sequential forward floating selection is deployed for choosing ten highly discriminative features. Lastly, the segmented prostate regions are classified into two groups, namely: tumor and normal classes by employing the support vector machine classifier. The experimental results elucidate that the proposed system is superior on the subject of accuracy, sensitivity, and specificity when compared with specific existing methods. Additionally, the proposed system also demonstrates a 94.75% accuracy. Moreover, this signifies the fact that the proposed method for analyzing the DCE data has shown prodigious prospects in the prostate carcinoma diagnosis.

Keywords

Prostate carcinoma Dynamic contrast-enhanced MRI Sequential forward floating selection Support vector machine Active contour model 

Notes

Acknowledgements

This work was supported by Ministry of Science and Technology, Taiwan, ROC, under Grant MOST 103-2221-E-224-016-MY3.

Funding

This research was partially funded by “Intelligent Recognition Industry Service Research Center” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan (Grant No. 108N04-2) and Ministry of Science and Technology in Taiwan (Grant No. MOST 103-2221-E-224-016-MY3).

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Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational Yunlin University of Science and TechnologyYunlinTaiwan
  2. 2.Department of Information Technology, School of Information Technology and EngineeringVellore Institute of TechnologyVelloreIndia
  3. 3.Department of UrologyNational Cheng Kung University HospitalTainanTaiwan
  4. 4.MobiSec LabSoonchunhyang UniversityAsan-SiRepublic of Korea
  5. 5.Department of Electronics and Communication EngineeringThe LNMIITJaipurIndia

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