Machine Learning in Computer-Aided Diagnosis of the Thorax and Colon in CT: A Survey

Kenji SUZUKI  

IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.4   pp.772-783
Publication Date: 2013/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.772
Print ISSN: 0916-8532
Type of Manuscript: INVITED SURVEY PAPER
machine learning in medical imaging,  computer-aided diagnosis,  classification,  pixel-based machine learning,  lung nodule,  colorectal polyp,  CT colonography,  

Full Text: FreePDF(3.1MB)

Computer-aided detection (CADe) and diagnosis (CAD) has been a rapidly growing, active area of research in medical imaging. Machine leaning (ML) plays an essential role in CAD, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require “learning from examples.” One of the most popular uses of ML is the classification of objects such as lesion candidates into certain classes (e.g., abnormal or normal, and lesions or non-lesions) based on input features (e.g., contrast and area) obtained from segmented lesion candidates. The task of ML is to determine “optimal” boundaries for separating classes in the multi-dimensional feature space which is formed by the input features. ML algorithms for classification include linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), multilayer perceptrons, and support vector machines (SVM). Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which uses pixel/voxel values in images directly, instead of features calculated from segmented lesions, as input information; thus, feature calculation or segmentation is not required. In this paper, ML techniques used in CAD schemes for detection and diagnosis of lung nodules in thoracic CT and for detection of polyps in CT colonography (CTC) are surveyed and reviewed.