Samuel J. Dwyer

A Survey of Preprocessing and Feature Extraction Techniques for Radiographic Images

Feature extraction is one of the more difficult steps in image pattern recognition. Some sources of difficulty are the presence of irrelevant information and the relativity of a feature set to a particular application. Several preprocessing techniques for enhancing selected features and removing irrelevant data are described and compared. The techniques include gray level distribution linearization, digital spatial filtering, contrast enhancement, and image subtraction. Also, several feature extraction techniques are illustrated. The techniques are divided into spatial and Fourier domain operations. The spatial domain operations of directional signatures and contour tracing are first described. Then, the Fourier domain techniques of frequency signatures and template matching are illustrated. Finally, a practical image pattern recognition problem is solved using some of the described techniques.

Automated Radiographic Diagnosis via Feature Extraction and Classification of Cardiac Size and Shape Descriptors

One goal of digital processing of radiographic images is to provide the radiologist with quantitative measurements of human anatomy as well as an indication as to whether or not this anatomy is within normal limits. A computer algorithm is described, designed to automatically detect, extract quantitative measurements from, and diagnose the cardiac projection present in full-size anteriorview chest radiographs. A normal-abnormal diagnosis is demonstrated utilizing abnormal data from five classes of heart disease. In addition, normal-abnormal as well as normal-differential diagnoses are demonstrated for the rheumatic heart disease class. A feature extraction algorithm is developed using several ad hoc techniques, some of which were adapted from other feature extraction uses. The extracted features are classified into diagnostic classes using linear and quadratic discriminant functions. A concurrent study of physician diagnostic accuracy is also undertaken using the averaged diagnostic rates of ten radiologists on a representative subset of the radiographs used in the computer study.

Computer analysis of chest radiographs

A method is described for the automatic recognition of congenital heart abnormalities via digitized images of posteroanterior (PA) view chest radiographs. a closed curve representing the hearts outline is determined by computer; measurements on the outline are extracted via the Fourier descriptor technique; and these measurements are used for normal-abnormal grouping via a modified maximum likelihood classification algorithm. The system has been implemented on a dec pdp-11/20 and is capable of performing real-time diagnoses at the rate of about two minutes per radiograph.

On the Sufficiency of Autocorrelation Functions as EEG Descriptors

Brain waves or EEGs are the seemingly random voltage fluctuations which appear on the surface of the scalp of humans and animals. The EEG Research Group at the University of Missouri has been interested in finding useful statistics with which to describe an EEG. In the past such calculations as the rms value of the wave, the autocorrelation function, etc., have been used to describe the process. It is convenient to use a stationary random process as a model for the EEG. Because the amplitude distribution of the EEG appears to be Gaussian, it has been suggested that a better model might be the normal stationary random process. This paper describes a test which was made to determine if the normal process is really a good model for the EEG.

Computer diagnosis of radiographic images

The potential of optical scanning equipment and digital computers for assisting or replacing human judgment in medical diagnosis has been recognized by investigators for some time. A number of efforts have been made, with varying degrees of success, in developing automatic techniques for recognizing and classifying blood cells, chromosome analysis and karyotyping, identifying leucocytes, and processing scintigram images obtained in nuclear medicine. These image analysis techniques are now being extended to the clinical specialty of diagnostic radiology where there is an urgent need to provide assistance in handling the several million radiographs read by radiologists each year. The need for computer-aided diagnosis in radiology is becoming increasingly urgent because of the expanding population and the continuing demand for improved quality of medical care. The use of computers in radiology can free the diagnostic radiologist from routine tasks while providing more accurate measurements that lead to consistent and reliable diagnoses.

Extraction of Connected Edges from Knee Radiographs

A computer algorithm is described that extracts the edge information from a digital image of an AP radiograph of the knee. Automated image field partitioning is used as a simplifying first step in the process. Methods that employ a priori knowledge are used to insure simple connected edges.

Digital techniques for image enhancement of radiographs

Abstract This paper deals with digital, position variant and invariant linear and nonlinear techniques for image enhancement (Rozenfeld, 1969). The radiographic system, the image processing and display system, and the human visual system are discussed. The enhancement techniques tailor image content of radiographs to the type of information most useful to the human visual system.

Computer processing of CT images: advances and prospects

Computerized tomography (CT) images are created by computer and as such are inherently amenable to computer image processing techniques. Advances have been made in the areas of alternative visualization (coronal views, etc.), image enhancement, feature extraction, and computer analysis of the extracted information. Further advances await imaginative application of these techniques and time; others will depend upon necessary advances in image processing methods.

An economical tonal display for interactive graphics and image analysis data

Abstract The use of tonal displays in image analysis and interactive graphics has always dictated the use of expensive refresh memories for the display output device. This has involved the use of high speed digital drums, multiple head discs, and analog storage tubes. Recently, the introduction of very long shift registers has allowed the designer to consider their use for refresh memories. A prototype display using 1024 bit MOS static shift registers has been developed. It has been shown that a reasonable cost versus performance tradeoff can be obtained. The first efforts has resulted in a 128 × 128 × 4 bit (64k) memory; it is now in the process of being expanded to 256 × 256 × 8 bits (512k). This memory is cost competitive with digital disc memories and both cost and performance competitive with storage tube scan converters.

Computer Analysis Of Radiographic Images

The significant steps in computer anal-ysis of radiographic images are 1) digitization of the x-ray image; 2) preprocessing of digital images; 3) extraction of significant features; and 4) automatic classification for normal, abnormal, and differential diagnosis. A typical digital image processing facility is described, including the needed interactive type digital displays. Techniques used for preprocessing radio-graphic images are detailed; typically, these are used to ensure a higher degree of success in the later stages of digital processing. Contour tracing and region enumeration algorithms are detailed for use in the com-puter analysis of radiographs. The important descriptive approach to the problem of feature extraction is provided along with illustrative examples. A case study of rheumatic and congenital heart disease is presented for the cardiac shape analysis of PA chest films.