Article citationsMore>>

Firjani, A., Khalifa, F., Elnakib, A., Gimel’farb, G., El-Ghar, M.A., Elmaghraby, A. and El-Baz, A. (2011) 3D automatic approach for precise segmentation of the prostate from diffusion-weighted magnetic resonance imaging. Proceedings of 18th Annual IEEE International Conference on Image Processing (ICIP), Brussels, 11-14 September 2011, 2285-2288.

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.