Pixel-based machine learning in medical imaging.

Kenji Suzuki

doi:10.1155/2012/792079

Abstract

Machine learning (ML) plays an important role in the medical imaging field, including medical image analysis and computer-aided diagnosis, 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 classification of objects such as lesions into certain classes (e.g., abnormal or normal, or lesions or nonlesions) based on input features (e.g., contrast and circularity) obtained from segmented object candidates. Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which use pixel/voxel values in images directly instead of features calculated from segmented objects as input information; thus, feature calculation or segmentation is not required. Because the PML can avoid errors caused by inaccurate feature calculation and segmentation which often occur for subtle or complex objects, the performance of the PML can potentially be higher for such objects than that of common classifiers (i.e., feature-based MLs). In this paper, PMLs are surveyed to make clear (a) classes of PMLs, (b) similarities and differences within (among) different PMLs and those between PMLs and feature-based MLs, (c) advantages and limitations of PMLs, and (d) their applications in medical imaging.

Highlights

Machine learning (ML) plays an essential role in the medical imaging field, including medical image analysis and computer-aided diagnosis (CAD) [1, 2], because objects such as lesions and organs in medical images may be too complex to be represented accurately by a simple equation; modeling of such complex objects often requires a number of parameters which have to be determined by data
One of the most popular uses of ML in medical image analysis is the classification of objects such as lesions into certain classes based on input features obtained from segmented object candidates (This class of ML is referred to featurebased ML.)
Because the pixel/voxel-based ML (PML) can avoid errors caused by inaccurate feature calculation and segmentation which often occur for subtle or complex objects, the performance of the PML can potentially be higher for such objects than that of common classifiers

Summary

Introduction

Machine learning (ML) plays an essential role in the medical imaging field, including medical image analysis and computer-aided diagnosis (CAD) [1, 2], because objects such as lesions and organs in medical images may be too complex to be represented accurately by a simple equation; modeling of such complex objects often requires a number of parameters which have to be determined by data. The dual-kernel approach, which employs central kernels and peripheral kernels in each layer [43], was proposed for distinction between lung nodules and nonnodules in chest radiographs [42, 43] and distinction between microcalcifications and other anatomic structures in mammograms [43] This dual-kernel-based convolution NN has several output units (instead of one or two output units in the standard convolution NN) for two-class classification. The input and output of the multilayer perceptron are pixel values in a given binary image that contains a single character (e.g., a, b, or c) and a class to which the given image belongs, respectively. Because character recognition with this type of the multilayer perceptron architecture is not shift-, rotation-, or scale-invariant, a large number of training samples is generally required. For details of feature-based classifiers, refer to one of many textbooks in pattern recognition such as [6,7,8, 10, 84]

Similarities and Differences

Applications of PML Algorithms in Medical Images

Advantages and Limitations of PML Algorithms

Findings

Conclusion