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

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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.

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Source: 2017 Statistics of Causes of Death in Taiwan (https://www.mohw.gov.tw/cp-3961-42866-2.html)

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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. Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan

    Chuan-Yu Chang & Hui-Ya Hu

  2. Department of Information Technology, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Kathiravan Srinivasan

  3. Department of Urology, National Cheng Kung University Hospital, Tainan, Taiwan

    Yuh-Shyan Tsai

  4. MobiSec Lab, Soonchunhyang University, Asan-Si, Republic of Korea

    Vishal Sharma

  5. Department of Electronics and Communication Engineering, The LNMIIT, Jaipur, India

    Punjal Agarwal

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  1. Chuan-Yu Chang
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Chang, CY., Srinivasan, K., Hu, HY. et al. SFFS–SVM based prostate carcinoma diagnosis in DCE-MRI via ACM segmentation. Multidim Syst Sign Process 31, 689–710 (2020). https://doi.org/10.1007/s11045-019-00682-3

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