Pixel-Based Machine Learning in Medical Imaging

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.

[1]  Shijun Wang,et al.  Improved classifier for computer-aided polyp detection in CT colonography by nonlinear dimensionality reduction. , 2008, Medical physics.

[2]  Sumit K. Shah,et al.  Computer aided characterization of the solitary pulmonary nodule using volumetric and contrast enhancement features. , 2005, Academic radiology.

[3]  J. Malley,et al.  Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees. , 2002, Medical physics.

[4]  T. Ishigaki,et al.  One-shot dual-energy subtraction imaging. , 1986, Radiology.

[5]  Ronald M Summers,et al.  Reduction of false positives on the rectal tube in computer-aided detection for CT colonography. , 2004, Medical physics.

[6]  Bram van Ginneken,et al.  Filter learning: Application to suppression of bony structures from chest radiographs , 2006, Medical Image Anal..

[7]  Kenji Suzuki,et al.  A Simple Neural Network Pruning Algorithm with Application to Filter Synthesis , 2001, Neural Processing Letters.

[8]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Vladimir Naumovich Vapni.  The Nature of Statistical Learning Theory , 1995 .

[10]  Lionel S. Zuckier,et al.  Pediatric Nuclear Medicine. 2nd ed , 1996 .

[11]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[12]  R S MACKAY,et al.  Radiological contrast enhancing methods. , 1958, Advances in biological and medical physics.

[13]  Lilla Böröczky,et al.  Feature Subset Selection for Improving the Performance of False Positive Reduction in Lung Nodule CAD , 2005, IEEE Transactions on Information Technology in Biomedicine.

[14]  J. Austin,et al.  Missed bronchogenic carcinoma: radiographic findings in 27 patients with a potentially resectable lesion evident in retrospect. , 1992, Radiology.

[15]  K. Doi,et al.  Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization. , 2005, Radiology.

[16]  Ronald M. Summers,et al.  Employing topographical height map in colonic polyp measurement and false positive reduction , 2009, Pattern Recognit..

[17]  K L Lam,et al.  Computer-aided detection of mammographic microcalcifications: pattern recognition with an artificial neural network. , 1995, Medical physics.

[18]  K. Doi,et al.  Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. , 2004, Academic radiology.

[19]  K. Doi,et al.  Experimental determination of subjective similarity for pairs of clustered microcalcifications on mammograms: observer study results. , 2006, Medical physics.

[20]  A.H.W. Nias,et al.  Advances in Biological and Medical Physics , 1966 .

[21]  S. Armato,et al.  Automated detection of lung nodules in CT scans: preliminary results. , 2001, Medical physics.

[22]  K. Doi,et al.  Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images. , 2002, Medical physics.

[23]  Alejandro F. Frangi,et al.  Model-based quantitation of 3-D magnetic resonance angiographic images , 1999, IEEE Transactions on Medical Imaging.

[24]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[25]  Kenji Suzuki,et al.  Efficient approximation of neural filters for removing quantum noise from images , 2002, IEEE Trans. Signal Process..

[26]  K Doi,et al.  Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks. , 1992, Medical physics.

[27]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[28]  K. Doi,et al.  Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images. , 2003, Medical physics.

[29]  Karen Drukker,et al.  Automated detection of lung nodules in CT scans: false-positive reduction with the radial-gradient index. , 2006, Medical physics.

[30]  K. Doi,et al.  False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network. , 2005, Academic radiology.

[31]  Heber MacMahon,et al.  Development and evaluation of a computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two-stage nodule enhancement with support vector classification. , 2011, Medical physics.

[32]  Ken-ichi Suzuki,et al.  Neural Filter with Selection of Input Features and Its Application to Image Quality Improvement of Medical Image Sequences , 2002 .

[33]  Sid Deutsch,et al.  A simplified version of Kunihiko Fukushima's neocognitron , 1981, Biological Cybernetics.

[34]  K Doi,et al.  Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network. , 1994, Medical physics.

[35]  Kenji Suzuki,et al.  Massive-training support vector regression and Gaussian process for false-positive reduction in computer-aided detection of polyps in CT colonography. , 2011, Medical physics.

[36]  Zhengrong Liang,et al.  Increasing computer-aided detection specificity by projection features for CT colonography. , 2010, Medical physics.

[37]  Kenji Suzuki,et al.  Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector , 2004, IEEE Transactions on Medical Imaging.

[38]  S. Armato,et al.  Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. , 2003, Medical physics.

[39]  Hiroyuki Yoshida,et al.  Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: Suppression of rectal tubes. , 2006, Medical physics.

[40]  Matthew T. Freedman,et al.  Artificial convolution neural network techniques and applications for lung nodule detection , 1995, IEEE Trans. Medical Imaging.

[41]  K. Doi,et al.  Investigation of psychophysical measure for evaluation of similar images for mammographic masses: preliminary results. , 2005, Medical physics.

[42]  Huai Li,et al.  A multiple circular path convolution neural network system for detection of mammographic masses , 2002, IEEE Transactions on Medical Imaging.

[43]  Russell C. Hardie,et al.  Performance analysis of a new computer aided detection system for identifying lung nodules on chest radiographs , 2008, Medical Image Anal..

[44]  Kenji Suzuki,et al.  Massive-Training Artificial Neural Network Coupled With Laplacian-Eigenfunction-Based Dimensionality Reduction for Computer-Aided Detection of Polyps in CT Colonography , 2010, IEEE Transactions on Medical Imaging.

[45]  Kunio Doi,et al.  Computer-aided diagnosis in medical imaging: Historical review, current status and future potential , 2007, Comput. Medical Imaging Graph..

[46]  S. Armato,et al.  Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography. , 2008, Medical physics.

[47]  Kesheng Wu,et al.  Fast connected-component labeling , 2009, Pattern Recognit..

[48]  Maryellen L Giger,et al.  Update on the potential role of CAD in radiologic interpretations: are we making progress?. , 2005, Academic radiology.

[49]  Tomaso A. Poggio,et al.  On Edge Detection , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Kenji Suzuki,et al.  Neural Edge Enhancer for Supervised Edge Enhancement from Noisy Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[51]  K. Doi,et al.  Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: localized search method based on anatomical classification. , 2006, Medical physics.

[52]  Ioannis Pitas,et al.  Digital Image Processing Algorithms and Applications , 2000 .

[53]  K. Doi,et al.  Computerized detection of intracranial aneurysms for three-dimensional MR angiography: feature extraction of small protrusions based on a shape-based difference image technique. , 2006, Medical physics.

[54]  Kunio Doi,et al.  Suppression of the contrast of ribs in chest radiographs by means of massive training artificial neural network , 2004, SPIE Medical Imaging.

[55]  Kunihiko Fukushima.  Neocognitron capable of incremental learning , 2004, Neural Networks.

[56]  Daniel F. Michaels.  Internal Organization of Classifier Networks Trained by Backpropagation , 1992, Int. J. Pattern Recognit. Artif. Intell..

[57]  Lubomir M. Hadjiiski,et al.  Bilateral analysis based false positive reduction for computer-aided mass detection. , 2007, Medical physics.

[58]  A. Murat Tekalp,et al.  Adaptive motion-compensated filtering of noisy image sequences , 1993, IEEE Trans. Circuits Syst. Video Technol..

[59]  Nikolas P. Galatsanos,et al.  A support vector machine approach for detection of microcalcifications , 2002, IEEE Transactions on Medical Imaging.

[60]  Bram van Ginneken,et al.  Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification , 2006, IEEE Transactions on Medical Imaging.

[61]  S Sakuma,et al.  One-shot dual-energy subtraction chest imaging with computed radiography: clinical evaluation of film images. , 1988, Radiology.

[62]  Kunio Doi,et al.  Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network , 2005, IEEE Transactions on Medical Imaging.

[63]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[64]  R. Glocker,et al.  Über die röntgenspektroskopische Bestimmung des Gewichtsanteiles eines Elementes in Gemengen und Verbindungen , 2022 .

[65]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[66]  Kenji Suzuki,et al.  Comparison of 2D and 3D views for evaluation of flat lesions in CT colonography. , 2010, Academic radiology.

[67]  Pasquale Delogu,et al.  Characterization of mammographic masses using a gradient-based segmentation algorithm and a neural classifier , 2007, Comput. Biol. Medicine.

[68]  K Doi,et al.  An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms. , 1996, Medical physics.

[69]  Guido Valli,et al.  Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms , 2003, IEEE Transactions on Information Technology in Biomedicine.

[70]  Lubomir M. Hadjiiski,et al.  Characterization of mammographic masses based on level set segmentation with new image features and patient information. , 2007, Medical physics.

[71]  M. Giger,et al.  Automated computerized classification of malignant and benign masses on digitized mammograms. , 1998, Academic radiology.

[72]  H. MacMahon,et al.  Performance of radiologists in detection of small pulmonary nodules on chest radiographs: effect of rib suppression with a massive-training artificial neural network. , 2009, AJR. American journal of roentgenology.

[73]  Kazuyoshi Itoh,et al.  Generalization of shift invariant neural networks: Image processing of corneal endothelium , 1996, Neural Networks.

[74]  Andrew R. Barron,et al.  Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.

[75]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[76]  Hiroyuki Yoshida,et al.  Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps , 2001, IEEE Transactions on Medical Imaging.

[77]  Matthew T. Freedman,et al.  Reduction of false positives in lung nodule detection using a two-level neural classification , 1996, IEEE Trans. Medical Imaging.

[78]  Berkman Sahiner,et al.  Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features. , 2009, Medical physics.

[79]  Alan D. Lopez,et al.  Mortality by cause for eight regions of the world: Global Burden of Disease Study , 1997, The Lancet.

[80]  Kenji Suzuki,et al.  CT colonography: advanced computer-aided detection scheme utilizing MTANNs for detection of "missed" polyps in a multicenter clinical trial. , 2009, Medical physics.

[81]  Akiko Kano,et al.  10．乳腺診断支援(CAD) , 2013 .

[82]  Walter Park,et al.  Prevalence of nonpolypoid (flat and depressed) colorectal neoplasms in asymptomatic and symptomatic adults. , 2008, JAMA.

[83]  C A Mistretta,et al.  Single-exposure dual-energy computed radiography: improved detection and processing. , 1990, Radiology.

[84]  H P Chan,et al.  Selection of an optimal neural network architecture for computer-aided detection of microcalcifications--comparison of automated optimization techniques. , 2001, Medical physics.

[85]  Berkman Sahiner,et al.  Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images , 1996, IEEE Trans. Medical Imaging.

[86]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[87]  K. Doi,et al.  Current status and future potential of computer-aided diagnosis in medical imaging. , 2005, The British journal of radiology.

[88]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.

[89]  G. Goodman,et al.  Lung cancer • 1: Prevention of lung cancer , 2002, Thorax.

[90]  John F. Hamilton,et al.  A Free Response Approach To The Measurement And Characterization Of Radiographic Observer Performance , 1977, Other Conferences.

[91]  Kunio Doi,et al.  Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN) , 2006, IEEE Transactions on Medical Imaging.

[92]  S. Armato,et al.  Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings. , 2002, Radiology.

[93]  B K Stewart,et al.  Single-exposure dual-energy computed radiography. , 1990, Medical physics.

[94]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[95]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[96]  Manfred H. Hueckel.  An Operator Which Locates Edges in Digitized Pictures , 1971, J. ACM.

[97]  Kenji Suzuki,et al.  Determining the receptive field of a neural filter , 2004, Journal of neural engineering.

[98]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[99]  Sung-Nien Yu,et al.  Detection of microcalcifications in digital mammograms using wavelet filter and Markov random field model , 2006, Comput. Medical Imaging Graph..

[100]  Gary J Whitman,et al.  Dual-energy digital subtraction chest radiography: technical considerations. , 2002, Current problems in diagnostic radiology.

[101]  Kenji Suzuki,et al.  Linear-time connected-component labeling based on sequential local operations , 2003, Comput. Vis. Image Underst..

[102]  Huai Li,et al.  Artificial convolution neural network for medical image pattern recognition , 1995, Neural Networks.

[103]  Jamshid Dehmeshki,et al.  Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images , 2009, IEEE Transactions on Biomedical Engineering.

[104]  J. Austin,et al.  Missed non-small cell lung cancer: radiographic findings of potentially resectable lesions evident only in retrospect. , 2003, Radiology.

[105]  Kenji Suzuki.  A supervised 'lesion-enhancement' filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD). , 2009, Physics in medicine and biology.

[106]  Kenji Suzuki,et al.  A Run-Based Two-Scan Labeling Algorithm , 2008, IEEE Transactions on Image Processing.

[107]  Berkman Sahiner,et al.  Computer aided detection of clusters of microcalcifications on full field digital mammograms. , 2006, Medical physics.

[108]  K. Doi,et al.  Determination of subjective similarity for pairs of masses and pairs of clustered microcalcifications on mammograms: comparison of similarity ranking scores and absolute similarity ratings. , 2007, Medical physics.

[109]  Akiko Kano,et al.  10．乳腺診断支援(CAD) , 2013 .

[110]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[111]  Claus Nebauer,et al.  Evaluation of convolutional neural networks for visual recognition , 1998, IEEE Trans. Neural Networks.