Neural Network Ensemble Based CAD System for Focal Liver Lesions from B-Mode Ultrasound |
| |
Authors: | Jitendra Virmani Vinod Kumar Naveen Kalra Niranjan Khandelwal |
| |
Institution: | 1. Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Waknaghat, Solan, 173234 Himachal Pradesh, India 2. Department of Electrical Engineering, Indian Institute of Technology-Roorkee, Roorkee, 247667 Uttarakhand, India 3. Department of Radiodiagnosis and Imaging, Post-Graduate Institute of Medical Education and Research, Sector-12, Chandigarh, 160012 India
|
| |
Abstract: | A neural network ensemble (NNE) based computer-aided diagnostic (CAD) system to assist radiologists in differential diagnosis between focal liver lesions (FLLs), including (1) typical and atypical cases of Cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small and large hepatocellular carcinoma (HCC) lesions, along with (3) normal (NOR) liver tissue is proposed in the present work. Expert radiologists, visualize the textural characteristics of regions inside and outside the lesions to differentiate between different FLLs, accordingly texture features computed from inside lesion regions of interest (IROIs) and texture ratio features computed from IROIs and surrounding lesion regions of interests (SROIs) are taken as input. Principal component analysis (PCA) is used for reducing the dimensionality of the feature space before classifier design. The first step of classification module consists of a five class PCA-NN based primary classifier which yields probability outputs for five liver image classes. The second step of classification module consists of ten binary PCA-NN based secondary classifiers for NOR/Cyst, NOR/HEM, NOR/HCC, NOR/MET, Cyst/HEM, Cyst/HCC, Cyst/MET, HEM/HCC, HEM/MET and HCC/MET classes. The probability outputs of five class PCA-NN based primary classifier is used to determine the first two most probable classes for a test instance, based on which it is directed to the corresponding binary PCA-NN based secondary classifier for crisp classification between two classes. By including the second step of the classification module, classification accuracy increases from 88.7 % to 95 %. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in differential diagnosis of FLLs. |
| |
Keywords: | Focal liver lesions B-mode ultrasound Texture analysis Neural network ensemble Computer-aided diagnostic system Principal component analysis |
本文献已被 SpringerLink 等数据库收录! |
|