Computer Vision and Decision Support

The preceding chapters describe basic mechanisms by which visual information is detected by the eye and brain, as well as fundamental principles of perception including texture and object recogniton and cognitive processing. In addition, Chaps. 7 and 8 discuss computer processing techniques that may make specific image features easier to perceive. In this chapter, we consider ways that computers can directly extract features from images (computer vision). and understand their meaning and support human cognition (decision support). For example, in medical imaging, computer-vision techniques may be used to detect and characterize a possible abnormality (such as a tumor mass) in an image of the breast and then artificial intelligence (AI) techniques may be employed to merge the extracted features into a diagnostic decision regarding the possibility of malignancy. Computer processing techniques include image processing, image segmentation, and feature extraction. Decision support tools include rule-based expert systems, discriminant analysis,, Bayesian methods, and neural networks among others.

[1]  Ernest L. Hall,et al.  Texture Measures for Automatic Classification of Pulmonary Disease , 1972, IEEE Transactions on Computers.

[2]  M. Moskowitz,et al.  Breast cancer missed by mammography. , 1979, AJR. American journal of roentgenology.

[3]  Ernest L. Hall,et al.  Computer Image Processing and Recognition , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  K. Doi,et al.  Image feature analysis and computer-aided diagnosis in digital radiography: automated analysis of sizes of heart and lung in chest images. , 1990, Medical physics.

[5]  P Armitage,et al.  Statistical methods for the identification and use of prognostic factors , 1974, International journal of cancer.

[6]  E. Krupinski,et al.  Computer-displayed eye position as a visual aid to pulmonary nodule interpretation. , 1990, Investigative radiology.

[7]  J. Hendriks,et al.  REDUCTION OF BREAST CANCER MORTALITY THROUGH MASS SCREENING WITH MODERN MAMMOGRAPHY First Results of the Nijmegen Project, 1975 - 1981 , 1984, The Lancet.

[8]  Roger C. Schank.  The Cognitive Computer: On Language, Learning, and Artificial Intelligence , 1984 .

[9]  I S Simor,et al.  Sensitivity and specificity of first screen mammography in the Canadian National Breast Screening Study: a preliminary report from five centers. , 1986, Radiology.

[10]  King-Sun Fu,et al.  Data Structures, Computer Graphics, and Pattern Recognition , 1977 .

[11]  K Doi,et al.  Image feature analysis and computer-aided diagnosis in digital radiography: automated delineation of posterior ribs in chest images. , 1991, Medical physics.

[12]  K. Wright.  The Road to the Global Village. , 1990 .

[13]  Perry L. Miller,et al.  The Image/Icon System: Voice Activated Intelligent Image Display for Radiologic Diagnosis , 1989 .

[14]  S. Dwyer,et al.  Direct computer diagnosis of rheumatic heart disease. , 1971, Radiology.

[15]  L L Fajardo,et al.  Mammogram interpretation by physician assistants. , 1987, AJR. American journal of roentgenology.

[16]  P. Miller.  Expert critiquing systems , 1986 .

[17]  Kunio Doi,et al.  Digital Image Processing: Optimal Spatial Filter For Maximization Of The Perceived Snr Based On A Statistical Decision Theory Model For The Human Observer , 1985, Medical Imaging.

[18]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[19]  Harry E. Pople,et al.  An Experimental Computer-Based Diagnostic Consultant for General Internal Medicine , 1982 .

[20]  Casimir A. Kulikowski,et al.  EXPERT: A System for Developing Consultation Models , 1979, IJCAI.

[21]  M. Giger,et al.  Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields. , 1988, Medical physics.

[22]  C. Metz.  ROC Methodology in Radiologic Imaging , 1986, Investigative radiology.

[23]  E. Shortliffe,et al.  Readings in medical artificial intelligence: the first decade , 1984 .

[24]  S Katsuragawa,et al.  Image feature analysis and computer-aided diagnosis in digital radiography: effect of digital parameters on the accuracy of computerized analysis of interstitial disease in digital chest radiographs. , 1990, Medical physics.

[25]  S ORAM,et al.  THE NURSE'S LOAD. , 1965, Lancet.

[26]  William B. Thompson,et al.  Computer Diagnosis of Pneumoconiosis , 1974, IEEE Trans. Syst. Man Cybern..

[27]  Robert M. Haralick,et al.  Mathematical Morphology Techniques For Image Processing Applications In Biomedical Imaging , 1988, Medical Imaging.

[28]  G S Lodwick,et al.  Towards computer analysis of pulmonary infiltration. , 1978, Investigative radiology.

[29]  W. J. Tuddenham.  Visual search, image organization, and reader error in roentgen diagnosis. Studies of the psycho-physiology of roentgen image perception. , 1962, Radiology.

[30]  E.H. Shortliffe,et al.  Knowledge engineering for medical decision making: A review of computer-based clinical decision aids , 1979, Proceedings of the IEEE.

[31]  Stephen Herman.  Feature-Size Dependent Selective Edge Enhancement Of X-Ray Images , 1988, Medical Imaging.

[32]  S Katsuragawa,et al.  Quantitative computer-aided analysis of lung texture in chest radiographs. , 1990, Radiographics : a review publication of the Radiological Society of North America, Inc.

[33]  P. Friedman,et al.  Radiologic errors in patients with lung cancer. , 1981, The Western journal of medicine.

[34]  Y. Wu,et al.  Potential usefulness of an artificial neural network for differential diagnosis of interstitial lung diseases: pilot study. , 1990, Radiology.

[35]  K Doi,et al.  Improvement in radiologists' detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis. , 1990, Investigative radiology.

[36]  E. Shortliffe.  Mycin: computer-based medical consultations , 1976 .

[37]  D. G. Swain.  Computer aided diagnosis of acute abdominal pain , 1986 .

[38]  M. Giger,et al.  Computerized detection of pulmonary nodules in digital chest images: use of morphological filters in reducing false-positive detections. , 1990, Medical physics.

[39]  C. A. Kulikowski,et al.  Artificial intelligence methods and systems for medical consultation , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  S Katsuragawa,et al.  Image feature analysis and computer-aided diagnosis in digital radiography: detection and characterization of interstitial lung disease in digital chest radiographs. , 1988, Medical physics.

[41]  S. Dwyer,et al.  Automated radiographic diagnosis via feature extraction and classification of cardiac size and shape descriptors. , 1972, IEEE transactions on bio-medical engineering.

[42]  H L Kundel,et al.  Notes: Feasibility of classifying disseminated pulmonary diseases based on their Fourier spectra. , 1973, Investigative Radiology.

[43]  Lester Kallsher,et al.  Factors Influencing False Negative Rates in Xeromammography , 1979 .

[44]  Weber Fp,et al.  A Case of Chronic Obstructive Jaundice: Focal Necroses in the Liver: Myeloid Transformation of the Spleen. , 1926 .

[45]  G. Lodwick,et al.  Computer Diagnosis of Primary Bone Tumors , 1963 .

[46]  S Katsuragawa,et al.  Image feature analysis and computer-aided diagnosis in digital radiography: classification of normal and abnormal lungs with interstitial disease in chest images. , 1989, Medical physics.

[47]  L W Bassett,et al.  Breast cancer detection: one versus two views. , 1987, Radiology.

[48]  Kunio Doi,et al.  Investigation of methods for the computerized detection and analysis of mammographic masses , 1990, Medical Imaging: Image Processing.

[49]  Ramesh S. Patil,et al.  Review of Causal Reasoning in Medical Diagnosis , 1986 .

[50]  I Zamiri,et al.  Diphtheria today. Some experiences in Iran. , 1970, Lancet.

[51]  P. Miller,et al.  ICON: a computer-based approach to differential diagnosis in radiology. , 1987, Radiology.

[52]  Anil K. Jain,et al.  Artificial Neural Networks , 2010 .

[53]  N. Day,et al.  EVALUATION OF SCREENING FOR BREAST CANCER IN A NON-RANDOMISED STUDY (THE DOM PROJECT) BY MEANS OF A CASE-CONTROL STUDY , 1984, The Lancet.

[54]  K. Neuberg,et al.  The Anatomy of Judgment , 1960 .

[55]  Kunio Doi,et al.  Neural network approach for differential diagnosis of interstitial lung diseases , 1990, Medical Imaging: Image Processing.

[56]  W. S. Topham,et al.  THE ROLE OF PERIPHERAL RESISTANCE IN CONTROLLING CARDIAC OUTPUT DURING EXERCISE * , 1964, Annals of the New York Academy of Sciences.

[57]  Edward H. Shortliffe,et al.  Chapter 3 – Consultation System , 1976 .

[58]  Satosi Watanabe,et al.  Pattern Recognition: Human and Mechanical , 1985 .

[59]  K Doi,et al.  Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography. , 1987, Medical physics.

[60]  L V Ackerman,et al.  Computer screening of xeromammograms: a technique for defining suspicious areas of the breast. , 1979, Computers and biomedical research, an international journal.

[61]  Stephen R. Baker,et al.  Manual of diagnostic imaging , 1988 .

[62]  R W Coe,et al.  BRISTOL GENERAL HOSPITAL , 1856, Association medical journal.

[63]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .

[64]  G. W. Gross,et al.  Neural networks in radiologic diagnosis. II. Interpretation of neonatal chest radiographs. , 1990, Investigative radiology.

[65]  Peter Szolovits,et al.  Causal Understanding of Patient Illness in Medical Diagnosis , 1981, IJCAI.

[66]  G. Gorry,et al.  Towards the simulation of clinical cognition. Taking a present illness by computer. , 1976, The American journal of medicine.

[67]  C E Kahn,et al.  PHOENIX. An expert system for selecting diagnostic imaging procedures. , 1987, Investigative radiology.

[68]  Jun-ichiro Toriwaki,et al.  Pattern recognition of chest X-ray images , 1973, Comput. Graph. Image Process..

[69]  P L Miller,et al.  Medical plan-analysis by computer: critiquing the pharmacologic management of essential hypertension. , 1984, Computers and biomedical research, an international journal.

[70]  S. Shapiro,et al.  Ten- to fourteen-year effect of screening on breast cancer mortality. , 1982, Journal of the National Cancer Institute.

[71]  M. Vernon.  The psychology of perception , 1962 .

[72]  L. Tabár,et al.  REDUCTION IN MORTALITY FROM BREAST CANCER AFTER MASS SCREENING WITH MAMMOGRAPHY Randomised Trial from the Breast Cancer Screening Working Group of the Swedish National Board of Health and Welfare , 1985, The Lancet.

[73]  Jack Sklansky,et al.  Automatic Detection of Suspicious Abnormalities in Breast Radiographs , 1977 .

[74]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[75]  J. Swets,et al.  Enhanced interpretation of diagnostic images. , 1988, Investigative radiology.

[76]  L. Kuhns,et al.  Decision making in imaging , 1989 .

[77]  J C Wandtke,et al.  Computerized search of chest radiographs for nodules. , 1986, Investigative radiology.

[78]  P L Miller,et al.  An expert system which critiques patient workup: modeling conflicting expertise. , 1984, Computers and biomedical research, an international journal.

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

[80]  K Doi,et al.  Localization of inter-rib spaces for lung texture analysis and computer-aided diagnosis in digital chest images. , 1988, Medical physics.

[81]  R A Greenes,et al.  Computer-aided diagnostic strategy selection. , 1986, Radiologic clinics of North America.

[82]  P. Stamps,et al.  Ambulatory Care Systems , 1978 .

[83]  J M Boone,et al.  Neural networks in radiologic diagnosis. I. Introduction and illustration. , 1990, Investigative radiology.

[84]  J. Barba,et al.  Edge Detection In Cytology Using Morphological Filters , 1989, Photonics West - Lasers and Applications in Science and Engineering.

[85]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[86]  M L Giger,et al.  Pulmonary nodules: computer-aided detection in digital chest images. , 1990, Radiographics : a review publication of the Radiological Society of North America, Inc.

[87]  Isabelle E. Magnin,et al.  Mammographic Texture Analysis: An Evaluation Of Risk For Developing Breast Cancer , 1986 .

[88]  G. Octo Barnett,et al.  A computer program to assist in the care of acute respiratory failure , 1973 .

[89]  E. Shortliffe.  Computer-based medical consultations: mycin (elsevier north holland , 1976 .

[90]  Barbara J. McNeil,et al.  Brigham and Women's Hospital Handbook of Diagnostic Imaging , 1986 .

[91]  Marshall F. Driggs.  Problem-directed and medical information systems , 1973 .

[92]  K. Doi,et al.  Computer-aided detection of microcalcifications in mammograms. Methodology and preliminary clinical study. , 1988, Investigative radiology.

[93]  R. Kruger,et al.  Automated computer screening of chest radiographs for pneumoconiosis. , 1976, Investigative radiology.

[94]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[95]  M L JOHNSON,et al.  Observer error; its bearing on teaching. , 1955, Lancet.

[96]  Maryellen L. Giger,et al.  Computer-vision schemes for lung and breast cancer detection , 1993 .

[97]  Marcus J. Smith.  ERROR AND VARIATION IN DIAGNOSTIC RADIOLOGY , 1968 .

[98]  S S Sagel,et al.  Computed tomography of the thorax. , 1978, Radiology.

[99]  H. E. Pople,et al.  Internist-I, an Experimental Computer-Based Diagnostic Consultant for General Internal Medicine , 1982 .

[100]  P J Haug,et al.  Automated management of screening and diagnostic mammography. , 1987, Radiology.

[101]  C. Boring,et al.  Cancer statistics, 1990 , 1990, CA: a cancer journal for clinicians.

[102]  M. Yaffe,et al.  Characterisation of mammographic parenchymal pattern by fractal dimension. , 1990, Physics in medicine and biology.

[103]  A. I. Cohn,et al.  Expert system-controlled image display. , 1989, Radiology.

[104]  H. Warner,et al.  EXPERIENCE WITH BAYE'S THEOREM FOR COMPUTER DIAGNOSIS OF CONGENITAL HEART DISEASE * , 1964, Annals of the New York Academy of Sciences.

[105]  Dana H. Ballard,et al.  A Ladder-Structured Decision Tree for Recognizing Tumors in Chest Radiographs , 1976, IEEE Transactions on Computers.

[106]  J L Semmlow,et al.  A fully automated system for screening xeromammograms. , 1980, Computers and biomedical research, an international journal.

[107]  O.D. Faugeras,et al.  Applications of stochastic texture field models to image processing , 1981, Proceedings of the IEEE.