amhsr-open access medicla research journals

Supervised Classification of Lymph Nodes based on ADC Maps Construction from Whole Body Diffusion Weighted MRI


Radhia Ferjaoui, Mohamed Ali Cherni, Nour El Houda Kraiem and Tarek Kraiem

Background and Aim: The aim of this study was to evaluate and analyze the different Apparent Diffusion Coefficient (ADC) values of components of heterogeneous lymph nodes by using the K-means technique, compared with a whole-lesion mean ADC value alone in discriminating benign and malignant pathologies. Methods: In this paper, we propose a new method based on functional information to recognize the malignancy of lymph nodes in DW MRI images. Twenty patients with a total of 102 lesions were included in this work, and the regions of interest (ROIs) were automatically extracted using the segmentation process based on the Chan-Vese algorithm. The functional information is obtained through the reconstruction of ADC maps with two diffusion factors: b-values at 0 and at 600 s.mm2 for each ROI. Then the classification by K-means into solid and non-solid parts was done and the feature means ADC values were calculated for each cluster separately. And the distinguishing between cancerous lesions and benignant was done by using the K-nearest neighbors classifier (K-NN). Results: The results showed that the mean ADC values (in 10-3.mm².s-1) of necrotic part 1.03±0.03 were significantly higher than those measured in the solid parts 0.84±0.02. The optimal ADC threshold value for differentiating benign from malignant lymph nodes was determined using the analysis of the receiver operating characteristic(ROC) at 1.12 * 10-3 .mm².s-1 with the sensitivity (SE), specificity (SP) and area under the curve (AUC) being 94.12%, 89.19% and 0.972%, respectively. And the mean ADC values of benign and malignant lymph nodes were 2.1 * 10-3 .mm².s-1 and 0.80±0.27 * 10-3 mm².s-1 respectively. The ADC values obtained for benign lymph nodes were higher ADC values than those in malignant lesions. Conclusion: Thus, the proposed computeraided diagnosis is a helpful tool for automatic lymph nodes classification into clusters and it can successfully distinguish solid from non-solid parts in lymph nodes from the Whole body. It can also help users in predicting lesions pathologies (malignant or benign) based on the computer-aided diagnosis (CAD) system based on the K-NN classifier with accuracy higher than 93.43% and F1_measure and Geometric-mean values reach respectively 96%, 86.84%, when used ROIs placed in the solid partitions.

Select your language of interest to view the total content in your interested language

Awards Nomination
20+ Million Readerbase
Abstracted/Indexed in

  • Include Baidu Scholar
  • CNKI (China National Knowledge Infrastructure)
  • EBSCO Publishing's Electronic Databases
  • Exlibris – Primo Central
  • Google Scholar
  • Hinari
  • Infotrieve
  • National Science Library
  • ProQuest
  • TdNet
  • African Index Medicus
Annals of Medical and Health Sciences Research The Annals of Medical and Health Sciences Research is a bi-monthly multidisciplinary medical journal.
Submit your Manuscript