SCIRP Mobile Website
Paper Submission

Why Us? >>

  • - Open Access
  • - Peer-reviewed
  • - Rapid publication
  • - Lifetime hosting
  • - Free indexing service
  • - Free promotion service
  • - More citations
  • - Search engine friendly

Free SCIRP Newsletters>>

Add your e-mail address to receive free newsletters from SCIRP.


Contact Us >>

Article citations


Viswanath, S., Bloch, B.N., Genega, E., Rofsky, N., Lenkinski, R., Chappelow, J., et al. (2008) A comprehensive segmentation, registration, and cancer detection scheme on 3 Tesla in vivo prostate DCE-MRI. Medical Image Computing and Computer-Assisted Intervention, 11, 662- 669.

has been cited by the following article:

  • TITLE: A diffusion-weighted imaging based diagnostic system for early detection of prostate cancer

    AUTHORS: Ahmad Firjani, Ahmed Elnakib, Fahmi Khalifa, Georgy Gimel’farb, Mohamed Abou El-Ghar, Adel Elmaghraby, Ayman El-Baz

    KEYWORDS: Prostate Cancer; 3D Markov-Gibbs Random Field; Nonrigid Registration; Diffusion-Weighted Imaging

    JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.6 No.3A, March 29, 2013

    ABSTRACT: A new framework for early diagnosis of prostate cancer using Diffusion-Weighted Imaging (DWI) is proposed. The proposed diagnostic approach consists of the following four steps to detect locations that are suspicious for prostate cancer: 1) In the first step, we isolate the prostate from the surrounding anatomical structures based on a Maximum A Posteriori (MAP) estimate of a new log-likelihood function that accounts for the shape priori, the spatial interaction, and the current appearance of prostate tissues and its background (surrounding anatomical structures); 2) In order to take into account any local deformation between the segmented prostates at different b-values that could occur during the scanning process due to local motion, a non-rigid registration algorithm is employed; 3) A KNN-based classifier is used to classify the prostate into benign or malignant based on three appearance features extracted from registered images; and 4) The tumor boundaries are determined using a level set deformable model controlled by the diffusion information and the spatial interactions between the prostate voxels. Preliminary experiments on 28 patients (17 malignant and 11 benign) resulted in 100% correct classification, showing that the proposed method is a promising supplement to current technologies (biopsy-based diagnostic systems) for the early diagnosis of prostate cancer.