Biomedical subjects

Heber MacMahon

Publications and source records attributed to Heber MacMahon.

24 records · Page 2Linked to original sources

PubMed2002

Computerized detection of pulmonary embolism in spiral CT angiography based on volumetric image analysis.

A fully automated method for computerized detection of pulmonary embolism in spiral computed tomography angiography was developed based on volumetric image analysis. The detection method is based on segmentation of pulmonary vessels to limit the search space, and analysis of several three-dimensional features inside segmented vessel volume. The features utilized are vascular size, local contrast based on mathematical morphology, degree of curvilinearity based on second derivatives, and geometric features such as volume and length. Detection results were obtained for 19 clinical data sets and the performance of the method was evaluated. Using the number and locations of thrombi diagnosed by radiologists as the gold standard, 100% sensitivity was achieved with 7.7 false positives per case, and 85% sensitivity was obtained with 2.6 false positives. For identification of all the positive cases as positive, i.e., detection of at least one thrombus per positive case, 1.9 false positives per case were obtained. These preliminary results suggest that the method has potential for fully automated detection of pulmonary embolism.

Algorithms↗

PubMed2002

Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images.

A novel automated computerized scheme has been developed to assist radiologists for their distinction between benign and malignant solitary pulmonary nodules on chest images. Our database consisted of 55 chest radiographs (33 primary lung cancers and 22 benign nodules). In this method, the location of a nodule was indicated first by a radiologist. The difference image with a nodule was produced by use of filters and then represented in a polar coordinate system. The nodule was segmented automatically by analysis of contour lines of the gray-level distribution based on the polar-coordinate representation. Two clinical parameters (age and sex) and 75 image features were determined from the outline, the image, and histogram analysis for inside and outside regions of the segmented nodule. Linear discriminant analysis (LDA) and knowledge about benign and malignant nodules were used to select initial feature combinations. Many combinations for subgroups of 77 features were evaluated as input to artificial neural networks (ANNs). The performance of ANNs with the selected 7 features by use of the round-robin test showed Az = 0.872, which was greater than Az = 0.854 obtained previously with the manual method (P= 0.53). The performance of LDA (Az = 0.886) was slightly improved compared to that of ANNs (P = 0.59) and was greater than that of the manual method (Az = 0.854) reported previously (P = 0.40). The high level of its performance indicates the potential usefulness of this automated computerized scheme in assisting radiologists as a second opinion for distinction between benign and malignant solitary pulmonary nodules on chest images.

Adult↗

PubMed2002

Detection of lung nodules on digital chest radiographs: potential usefulness of a new contralateral subtraction technique.

PURPOSE: To evaluate the potential usefulness of a contralateral subtraction technique developed for radiologists' performance in the detection of subtle lung nodules on chest radiographs. MATERIALS AND METHODS: Fifty chest radiographs (25 normal and 25 abnormal with a subtle lung nodule) that were digitized with a 0.175-mm pixel size and 4,096 gray levels were used. Twelve radiologists (10 attending and two residents) participated in observer tests and read both original and contralateral subtraction images with a sequential testing method. Radiologists' performance was evaluated by means of receiver operating characteristic analysis with use of a continuous rating scale. The beneficial and detrimental effects of the contralateral subtraction technique on the radiologists' performance were also evaluated. RESULTS: The area under the receiver operating characteristic curve values obtained without and with contralateral subtraction images were 0.926 and 0.962, respectively. Results indicated that the contralateral subtraction images significantly (P <.05) improved diagnostic accuracy, particularly for radiologists with limited experience. CONCLUSION: The contralateral subtraction technique can assist radiologists in the correct identification of subtle lung nodules on chest radiographs.

Humans↗

PubMed2002

Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings.

PURPOSE: To compare clinical, histopathologic, and imaging features of lung cancers missed at low-radiation-dose helical computed tomography (CT). MATERIALS AND METHODS: Eighty-three primary lung cancers were found during an annual low-dose CT screening program and confirmed histopathologically at either surgery or biopsy. Thirty-two of these lung cancers were missed on 39 CT scans: on 23 scans owing to detection errors and on 16 owing to interpretation errors. The clinical characteristics, CT features, and histopathologic findings of these missed lung cancers were correlated. RESULTS: All missed cancers were intrapulmonary, and 28 (88%) were stage IA. All 20 detection errors occurred in cases of adenocarcinoma, 17 (85%) of which were well-differentiated tumors and 11 (55%) of which were in nonsmoking women. The mean size of cancers missed owing to detection error, 9.8 mm, was smaller than that of cancers missed owing to interpretation error, 15.9 mm (P <.001). In the detection error group, the percentages of nodules with ground-glass opacity (91%) or judged to be subtle (91%) were greater than those of nodules in the interpretation error group (38% and 25%, respectively) (P <.001). In the detection error group, 83% (19/23) of cancers were overlapped with, obscured by, or similar in appearance to normal structures such as pulmonary vessels. On 14 of the 16 CT scans with which there were interpretation errors, the CT findings mimicked benign disease, and the patients also had underlying lung disease, such as tuberculosis, emphysema, or lung fibrosis. CONCLUSION: The lung cancers missed at low-dose CT screening in this series generally were very subtle and appeared as small faint nodules, overlapping normal structures, or opacities in a complex background of other disease.

Adenocarcinoma↗

PubMed2002

Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program.

PURPOSE: To evaluate the performance of a fully automated computerized method for the detection of lung nodules in computed tomographic (CT) scans in the identification of lung cancers that may be missed during visual interpretation. MATERIALS AND METHODS: A database of 38 low-dose CT scans with 50 lung nodules was obtained from a lung cancer screening program. Thirty-eight of the nodules represented biopsy-confirmed lung cancers that had not been reported during initial clinical interpretation. A computer detection method that involved the use of gray-level thresholding techniques to identify three-dimensionally contiguous structures within the lungs was applied to the CT data. Computer-extracted volume was used to determine whether a structure became a nodule candidate. A rule-based scheme and a cascaded automated classifier were applied to the set of nodule candidates to distinguish actual nodules from areas of normal anatomy. Overall performance of the computer detection method was evaluated with free-response receiver operating characteristic (FROC) analysis. RESULTS: At a specific operating point on the FROC curve, the method achieved a sensitivity of 80% (40 of 50 nodules), with an average of 1.0 false-positive detection per section. Missed cancers were detected by the computerized method with a sensitivity of 84% (32 of 38 nodules) and a false-positive rate of 1.0 per section. CONCLUSION: With an automated lung nodule detection method, a large fraction (84%, 32 of 38) of missed cancers in a database of low-dose CT scans were detected correctly.

Aged↗

PubMedDate not supplied

Computer-aided diagnosis in chest radiography: results of large-scale observer tests at the 1996-2001 RSNA scientific assemblies.

Since 1996, computer-aided diagnosis (CAD) schemes have been presented as interactive demonstrations on computer workstations at each scientific assembly of the Radiological Society of North America. The schemes involved (a) detection of pulmonary nodules, (b) temporal subtraction, (c) detection of interstitial lung disease, (d) differential diagnosis of interstitial lung disease, and (e) distinction between benign and malignant pulmonary nodules on chest radiographs. Large-scale observer tests were carried out to examine how radiologists can benefit from CAD systems. Observer performance was evaluated by analysis of receiver operating characteristic (ROC) curves. The statistical significance of the difference between the areas under the ROC curves without and with CAD was analyzed with the Student t test. In all of the tests, the diagnostic accuracy of the radiologists in total improved significantly when CAD was used. This result provides additional evidence that CAD has the potential to improve the performance of radiologists in their decision-making process in interpreting chest radiographs.

Diagnosis, Computer-Assisted↗