Biomedical subjects

Jerry A Thomas

Publications and source records attributed to Jerry A Thomas.

3 recordsLinked to original sources

PubMed2005

Contrast-detail phantom scoring methodology.

Published results of medical imaging studies which make use of contrast detail mammography (CDMAM) phantom images for analysis are difficult to compare since data are often not analyzed in the same way. In order to address this situation, the concept of ideal contrast detail curves is suggested. The ideal contrast detail curves are constructed based on the requirement of having the same product of the diameter and contrast (disk thickness) of the minimal correctly determined object for every row of the CDMAM phantom image. A correlation and comparison of five different quality parameters of the CDMAM phantom image determined for obtained ideal contrast detail curves is performed. The image quality parameters compared include: (1) contrast detail curve--a graph correlation between "minimal correct reading" diameter and disk thickness; (2) correct observation ratio--the ratio of the number of correctly identified objects to the actual total number of objects multiplied by 100; (3) image quality figure--the sum of the product of the diameter of the smallest scored object and its relative contrast; (4) figure-of-merit--the zero disk diameter value obtained from extrapolation of the contrast detail curve to the origin (e.g., zero disk diameter); and (5) k-factor--the product of the thickness and the diameter of the smallest correctly identified disks. The analysis carried out showed the existence of a nonlinear relationship between the above parameters, which means that use of different parameters of CDMAM image quality potentially can cause different conclusions about changes in image quality. Construction of the ideal contrast detail curves for CDMAM phantom is an attempt to determine the quantitative limits of the CDMAM phantom as employed for image quality evaluation. These limits are determined by the relationship between certain parameters of a digital mammography system and the set of the gold disks sizes in the CDMAM phantom. Recommendations are made on selections of CDMAM phantom regions which should be used for scoring at different image quality and which scoring methodology may be most appropriate. Special attention is also paid to the use of the CDMAM phantom for image quality assessment of digital mammography systems particularly in the vicinity of the Nyquist frequency.

Algorithms↗

PubMed2004

Combination of digital mammography with semi-automated 3D breast ultrasound.

This paper describes work aimed at combining 3D ultrasound with full-field digital mammography via a semi-automatic prototype ultrasound scanning mechanism attached to the digital mammography system gantry. Initial efforts to obtain high x-ray and ultrasound image quality through a compression paddle are proving successful. Registration between the x-ray mammogram and ultrasound image volumes is quite promising when the breast is stably compressed. This prototype system takes advantage of many synergies between the co-registered digital mammography and pulse-echo ultrasound image data used for breast cancer detection and diagnosis. In addition, innovative combinations of advanced US and X-ray applications are being implemented and tested along with the basic modes. The basic and advanced applications are those that should provide relatively independent information about the breast tissues. Advanced applications include x-ray tomosynthesis, for 3D delineation of mammographic structures, and non-linear elasticity and 3D color flow imaging by ultrasound, for mechanical and physiological information unavailable from conventional, non-contrast x-ray and ultrasound imaging.

Adult↗

PubMed2002

Computer-aided diagnosis of masses with full-field digital mammography.

RATIONALE AND OBJECTIVES: The authors developed and evaluated a method of computer-aided diagnosis (CAD) for mass detection with full-field digital mammography (FFDM). MATERIALS AND METHODS: The new CAD method for FFDM employs adaptive, nonlinear multiscale processing and hybrid classification methods. The major strategies are (a) to "standardize" the mammographic image before it is input to the analysis modules, (b) to adapt the segmentation of suspicious regions adapt to accommodate different characteristics of masses and mammograms, and (c) to use combined "hard" and "soft" decision making in discriminating between mass and normal tissue regions. Two data sets of diagnostic FFDM mammograms were used. The training data set includes 36 normal and 24 abnormal mammograms (34 masses), and the testing data set includes 24 normal and 10 abnormal mammograms (10 masses). The tumors in this diagnostic database were more subtle and difficult to detect than those in screening databases the authors have used before. RESULTS: With the limited database and a partial optimization, a sensitivity of 91% was obtained in training, with a false-positive rate of 3.21 per image. At this trained operating point of the CAD system, six of 10 subtle masses were detected in testing. CONCLUSION: The CAD algorithms developed in screen-film mammography can be modified for FFDM. More data analysis and system optimization and evaluation will be needed before CAD can be integrated efficiently into the performance of FFDM.

Algorithms↗