Total views : 233

Anatomical Visions of Prostate Cancer in Different Modalities

Affiliations

  • UIET, Panjab University, Chandigarh - 160014, Punjab, India

Abstract


Objectives: Prostate cancer (CaP) is the second utmost diagnosed cancer among aged men all over the world. CaP contributes as a main health issue for men, posing a challenge to oncologists, urologists, and radiologists for diagnosis purpose. Methods: There are several ways to delineate the prostate capsule using different imaging modalities, which act as one of the vital step for development of Computer Aided Diagnosis (CAD) system. Findings: In order to assist novice researchers for CaP diagnosis, this paper presents the general working theme of CAD system and highlights the different classes of existing prostate segmentation techniques. In order to study different cancerous or non-cancerous anatomical views of CaP in different modalities, the key role of this paper is to fill the gap between the actual CaP view and beginner perception about CaP Region of Interest (ROI). Application/Improvement: Various research gaps that are current open challenges for competent and effective development of Computer Aided Diagnosis (CAD) system for CaP are also a part of this paper.

Keywords

Cancer, CAD, Diagnosis, Modality, Prostate.

Full Text:

 |  (PDF views: 153)

References


  • Weinreb JC. Prostate Imaging and Reporting Data SystemPI-RADS. European Urology. 2016; 69:16–40.
  • Ghose S, Oliver A, Marti R, Llado X, Vilanova JC, Freixenet J, Mitra J, Sidibe D, Meriaudeau F. A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images. Computer methods and programs in biomedicine. 2012 Oct; 108(1):262–87.3
  • Ghose S, Oliver A, Mitra J, Martí R, Llado X, Freixenet J, Sidibe D, Vilanova JC, Comet J, Meriaudeau F. A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images. Medical Image Analysis. 2013 Aug; 17(6):587–600.
  • Acosta O, Dowling J, Drean G, Simon A, De Crevoisier R, Haigron P. Multi-atlas-based segmentation of pelvic structures from CT scans for planning in prostate cancer radiotherapy. Springer US. 2014; 623–56.
  • Peng Y, Shen D, Liao S, Turkbey B, Rais‐Bahrami S, Wood B, Karademir I, Antic T, Yousef A, Jiang Y, Pinto PA. MRIbased prostate volume‐adjusted prostate‐specific antigen in the diagnosis of prostate cancer. Journal of Magnetic Resonance Imaging. 2015 Dec; 42(6):1733–9.
  • SVM with feature selection and smooth prediction in images: Application to CAD of prostate cancer. Available from: http://ieeexplore.ieee.org/document/7025455/. Date Accessed: 27/10/2014.
  • Salman S, Ma Z, Mohanty S, Bhele S, Chu YT, Knudsen B, Gertych A. A machine learning approach to identify prostate cancer areas in complex histological images. Springer International Publishing. 2014; 295–306.
  • Imaging in Prostate Cancer. Available from: http://www.medscape.com/viewarticle/742986_1. Date accessed: 2011.
  • Lemaitre G, Marti R, Freixenet J, Vilanova JC, Walker PM, Meriaudeau F. Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review. Computers in Biology and Medicine. 2015 May; 60:8–31.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.