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M T Freedman

Publications and source records attributed to M T Freedman.

At least 19 recordsLinked to original sources
PubMed1998

Anatomic region-based dynamic range compression for chest radiographs using warping transformation of correlated distribution.

The purpose of this paper is to investigate the effectiveness of our novel dynamic range compression (DRC) for chest radiographs. The purpose of DRC is to compress the gray scale range of the image when using narrow dynamic range viewing systems such as monitors. First, an automated segmentation method was used to detect the lung region. The combined region of mediastinum, heart, and subdiaphragm was defined based on the lung region. The correlated distributions, between a pixel value and its neighboring averaged pixel value, for the lung region and the combined region were calculated. According to the appearance of overlapping of two distributions, the warping function was decided. After pixel values were warped, the pixel value range of the lung region was compressed while preserving the detail information, because the warping function compressed the range of the averaged pixel values while preserving the pixel value range for the pixels which had had the same averaged pixel value. The performance was evaluated with our criterion function which was the contrast divided by the moment, where the contrast and the moment represent the sum of the differences between the pixel values and the averaged values of eight pixels surrounding that pixel, and the sum of the differences between the pixel values and the averaged value of all pixels in the region-of-interest, respectively. For 71 screening chest images from Johns Hopkins University Hospital (Baltimore, MD), this method improved our criterion function at 11.7% on average. The warping transformation algorithm based on the correlated distribution was effective in compressing the dynamic range while simultaneously preserving the detail information.

Humans↗
PubMed1998

Automated segmentation of anatomic regions in chest radiographs using an adaptive-sized hybrid neural network.

The purposes of this research are to investigate the effectiveness of our novel image features for segmentation of anatomic regions such as the lungs and the mediastinum in chest radiographs and to develop an automatic computerized method for image processing. A total of 85 screening chest radiographs from Johns Hopkins University Hospital were digitized to 2 K by 2.5 K pixels with 12 bit gray scale. To reduce the amount of information, the images were smoothed and subsampled to 256 by 310 pixels with 8 bit. The determination approach consists of classifying each pixel into two anatomic classes (lung and others) on the basis of several image features: (1) relative pixel address (Rx, Ry) based on lung edges extracted through image processing using profile, (2) density normalized from lungs and mediastinum density, and (3) histogram equalized entropy. The combinations of image features were evaluated using an adaptive-sized hybrid neural network consisting of an input, a hidden, and an output layer. Fourteen images were used for the training of the neural network and the remaining 71 images for testing. Using four features of relative address (Rx, Ry), normalized density, and histogram equalized entropy, the neural networks classified lungs at 92% accuracy against test images following the same rules as for the training images.

Biophysical Phenomena↗
PubMed1997

Image processing in digital radiography.

Image processing is a critical part of obtaining high-quality digital radiographs. Fortunately, the user of these systems does not need to understand image processing in detail, because the manufacturers provide good starting values. Because radiologists may have different preferences in image appearance, it is helpful to know that many aspects of image appearance can be changed by image processing, and a new preferred setting can be loaded into the computer and saved so that it can become the new standard processing method. Image processing allows one to change the overall optical density of an image and to change its contrast. Spatial frequency processing allows an image to be sharpened, improving its appearance. It also allows noise to be blurred so that it is less visible. Care is necessary to avoid the introduction of artifacts or the hiding of mediastinal tubes.

Humans↗
PubMed1997

Digital radiography of the chest.

Digital radiography is an appropriate method for both bedside and in-department chest radiographs. Its major advantage in bedside chest radiography is its control of the displayed optical density of these radiographs. With dynamic range control processing, it improves the visibility of tubes and lines superimposed on the mediastinal tissues. When used for in-department chest radiography, it may offer slight advantages in the evaluation of disease in the mediastinum, but in general is equivalent to film-screen chest radiography. The main reasons for using digital chest radiography for in-department chest radiographs relate mainly to its use as a data entry point method of projection radiography for high-quality teleradiology or for its use in a picture archiving and communication system. Apart from these advantages, there is no reason to change from conventional to digital chest radiographs. Digital radiographs are, with certain systems, printed at smaller than life size. Because of this, there is a necessary period of learning as radiologists adjust to the new image size. The most important change in radiologists' work pattern appears to be the need to sit closer to the film. Findings of disease are smaller, but, with experience, just as easy to see.

Humans↗
PubMed1997

Storage phosphor digital mammography.

Digital mammography using storage phosphor CR is still in the investigational stage. It is the only digital mammography system that has been tested in preliminary clinical trials with promising early results. Further clinical studies are needed to assess the impact of the limited spatial resolution of storage phosphor technology on its application as a digital screening mammography system. Further studies also are needed to determine the optimum image processing parameters needed in digital mammography.

Breast Neoplasms↗
PubMed1997

Update in digital mammography.

Digital mammography is a rapidly developing technology that has great potential to improve upon and ultimately replace conventional film-screen mammography for the early detection of breast cancer. This article reviews current progress in digital mammographic systems, computer-aided diagnostic programs, and artificial neural networks. Digital mammographic systems are currently in an investigational phase only. Large-scale clinical trials are needed in all areas of digital mammography before this exciting new technology can be implemented outside of research centers.

Artificial Intelligence↗
PubMed1995

Differentiation between nodules and end-on vessels using a convolution neural network architecture.

In recent years, many computer-aided diagnosis schemes have been proposed to assist radiologists in detecting lung nodules. The research efforts have been aimed at increasing the sensitivity while decreasing the false-positive detections on digital chest radiographs. Among the problems of reducing the number of false positives, the differentiation between nodules and end-on vessels is one of the most challenging tasks performed by computer. Most investigators have used a conventional two-stage pattern recognition approach, ie, feature extraction followed by feature classification. The performance of this approach depends totally on good feature definition in the feature extraction stage. Unfortunately, suitable feature definition and corresponding extraction implementation algorithms proved to be very difficult to define and specify. A convolution neural network (CNN) architecture, trained by direct connection to the raw image is proposed to tackle the problem. The CNN, which uses locally responsive activation function, is directly and locally connected to the raw image. The performance of the CNN is evaluated in comparison to an expert radiologist. We used the receiver operating characteristics (ROC) method with area under the curve (Az) as the performance index to evaluate all the simulation results. The CNN showed superior performance (Az = 0.99) to the radiologist's (Az = 0.83). The CNN approach can potentially be applied to other applications, such as the differentiation of film defects and microcalcifications in mammography, in which the image features are difficult to define or not known a priori.

Algorithms↗
PubMed1995

Classification of microcalcifications in radiographs of pathologic specimens for the diagnosis of breast cancer.

RATIONALE AND OBJECTIVES: Early detection of breast cancer depends on accurate classification of microcalcifications. We have developed a computer vision system that has the potential to classify microcalcifications objectively and consistently to aid radiologists in diagnosing breast cancer. METHODS: A convolution neural network (CNN) was used to classify benign and malignant microcalcifications in radiographs of pathologic specimens. Digital images were acquired by digitizing radiographs at a high resolution of 21 microns x 21 microns. RESULTS: Eighty regions of interest selected from digitized radiographs of pathologic specimens were used for training and testing of the neural network system. The CNN achieved an Az value (area under the receiver operating characteristic curve) of 0.90 in classifying clusters of microcalcifications associated with benign and malignant processes. CONCLUSION: Classification of microcalcifications in pathologic specimens for diagnosis of breast cancer was achieved at a high level in our computer vision system, which consists of high-resolution digitization of mammograms and a CNN.

Breast Neoplasms↗
PubMed1993

Automatic lung nodule detection using profile matching and back-propagation neural network techniques.

The potential advantages of using digital techniques instead of film-based radiography have been discussed extensively for the past 10 years. A major future application of digital techniques is computer-assisted diagnosis: the use of computer techniques to assist the radiologist in the diagnostic process. One aspect of this assistance is computer-assisted detection. The detection of small lung nodule has been recognized as a clinically difficult task for many years. Most of the literature has indicated that the rate for finding lung nodules (size range from 3 mm to 15 mm) is only approximately 65%, in those cases in which the undetected nodules could be found retrospectively. In recent published research, image processing techniques, such as thresholding and morphological analysis, have been used to enhance true-positive detection. However, these methods still produce many false-positive detections. We have been investigating the use of neural networks to distinguish true-positives nodule detections among those areas of interest that are generated from a signal enhanced image. The initial results show that the trained neural networks program can increase true-positive detections and moderately reduce the number of false-positive detections. The program reported here can perform three modes of lung nodule detection: thresholding, profile matching analysis, and neural network. This program is fully automatic and has been implemented in a DEC 5000/200 (Digital Equipment Corp, Maynard, MA) workstation. The total processing time for all three methods is less than 35 seconds. In this report, key image processing techniques and neural network for the lung nodule detection are described and the results of this initial study are reported.

Algorithms↗
PubMed1993

Application of artificial neural networks for reduction of false-positive detections in digital chest radiographs.

A methodology based on the fuzzy set theory and the convolution neural network (CNN) architecture is proposed to tackle the problem of reducing false-positive rate in automatic lung nodule detection. The CNN which simulates human visual mechanism was trained by a supervised back-propagation algorithm based on fuzzy membership functions. The training and testing database consists of image blocks (each 32 x 32 pixels) of suspected lung nodule areas (nodule candidates) which were generated from our pre-scanning program [1]. A linguistic label was assigned to each nodule candidate of the training set, then the label was converted to a membership value through a pre-defined membership function and used as teaching signal (desired outputs) during the network learning. Before the nodule candidate was fed to the network input, it was pre-processed to reduce the complex background noise and the contrast discrepancy resulted from film development. During the network testing phase, a defuzzification process was applied to decipher the trained network's output triggered by the nodule candidate in the testing set. Finally, a Receiver Operating Characteristic (ROC) analysis was used to evaluate the CNN's performance based on the defuzzified output of the testing database. Preliminary results showed an average Az (the performance index) of 0.84 which is equivalent to 0.80 true-positive detection (sensitivity) with an average 2-3 false-positive detections per chest image.

Computer Simulation↗
PubMed1991

Evaluation of the painful shoulder. A prospective comparison of magnetic resonance imaging, computerized tomographic arthrography, ultrasonography, and operative findings.

Twenty-one patients who had had pain in the shoulder for more than three months were evaluated with ultrasonography and magnetic resonance imaging followed by computerized tomographic arthrography. The results of the imaging studies were then compared with the operative findings. Magnetic resonance imaging was found to be the most useful modality for establishment of the etiology of pain in the shoulder due to disease of the rotator cuff, instability associated with abnormality of the glenoid labrum, subacromial impingement, stenosis of the coracoacromial arch, and osteoarthrosis of either the glenohumeral or the acromioclavicular joint. The accuracy of magnetic resonance imaging was found to depend on both the operator and the technique and was decreased in extremely obese patients, due to difficulties in positioning, and in patients who had had a previous operation. Magnetic resonance imaging was more accurate than either computerized tomographic arthrography or ultrasonography in identifying partial-thickness tears (intrasubstance changes in the rotator cuff). Magnetic resonance imaging provided the same level of accuracy as computerized tomographic arthrography in the detection of abnormalities of the glenoid labrum.

Adolescent↗
PubMed1990

Magnetic resonance imaging of peripheral soft tissue hemangiomas.

Ten patients with soft tissue hemangiomas outside the central nervous system were studied with MR imaging. Eight patients were studied at 1.5 Tesla (T) with T1-weighted and triple echo T2-weighted sequences. Two additional patients were imaged on a 0.5-T system. The MR images were correlated with images from other modalities. Histologic diagnosis was obtained in all cases. It was found that prolonged T2-weighted imaging together with standard spin echo T1 and T2 pulse sequences is a good substitute for contrast-enhanced CT and arteriographic evaluation of soft tissue hemangiomas.

Adolescent↗
PubMed1987

Post-traumatic osteochondroma.

Osteochondromas can arise as primary, spontaneous tumors of bone or in previously irradiated bone. We report a patient who developed an osteochondroma of bone which arose secondary to previous trauma. This has not previously been reported.

Adolescent↗
PubMed1985

Policies and attitudes toward the pregnant radiology resident.

To evaluate attitudes and policies toward pregnant radiology residents, a questionnaire was sent to the chiefs of radiology residency programs across the country. A return rate of 76.4% and a response rate of 75.4% were achieved. A large majority of the respondents indicated that schedule changes would be made to avoid excessive exposure of a pregnant resident to radiation. The accommodations they suggest are reviewed, and suggestions are made that would help alleviate some of the stress and conflicts that invariably arise when a resident becomes pregnant.

Attitude↗