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

Chuan Yu Chang, Kathiravan Srinivasan, Hui Ya Hu, Yuh Shyan Tsai, Vishal Sharma, Punjal Agarwal

Research output: Contribution to journalArticle

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)689-710
Number of pages22
JournalMultidimensional Systems and Signal Processing
Issue number2
Publication statusPublished - 2020 Apr 1

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Artificial Intelligence
  • Applied Mathematics

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