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Biomedical subjects

G M Rojas

Publications and source records attributed to G M Rojas.

5 recordsLinked to original sources
PubMed2000

Quantitation of T2 lesion load in patients with multiple sclerosis: a novel semiautomated segmentation technique.

RATIONALE AND OBJECTIVES: The authors designed a segmentation technique that requires only minimal operator input at the initial and final supervision stages of segmentation and has computer-driven segmentation as the primary determinant of lesion boundaries. The technique was applied to compute total T2-hyperintense lesion volumes in patients with multiple sclerosis (MS). A semi-automated segmentation technique is presented and shown to have a test-retest reliability of <5%. MATERIALS AND METHODS: The method used a single segmented section with MS lesions. A probabilistic neural net performed segmentation into four tissue classes after supervised training. This reference section was deconstructed into the entire set of possible 4 x 4-pixel subregions, which was used to segment all-brain sections in steps of 4 x 4-pixel, adjacent image blocks. Intra- and interimage variabilities were tested by using 3-mm-thick, T2-weighted, dual-echo, spin-echo MR images from five patients, each of whom was imaged twice on the same day. Five different reference sections and three temporally separated. training sessions involving the same reference section were used to test the segmentation technique. RESULTS: The coefficient of variation ranged from 0.013 to 0.068 (mean +/- standard deviation, 0.037 +/- 0.039) for results from five different reference sections for each brain and from 0.007 to 0.037 (mean, 0.027 +/- 0.021) for brains segmented with the same reference section on three temporally separated occasions. Test-retest (intra-imaging) reliability did not exceed 5% (except for a small lesion load of 1 cm3 in one patient). Interimaging differences were approximately 10%. CONCLUSION: The segmentation technique yielded intra-imaging variabilities (2%-3%, except for very small MS lesion loads) that compare favorably with previously published results. New repositioning techniques that minimize imaging-repeat imaging variability could make this approach attractive for resolving MS lesion detection problems.

Algorithms↗
PubMed1997

Quantitation of T2 lesion load in multiple sclerosis with magnetic resonance imaging: a pilot study of a probabilistic neural network approach.

RATIONALE AND OBJECTIVES: To quantitate multiple sclerosis (MS) lesions in the brain by using computerized techniques. METHODS: MS lesions from five patients were quantitated with magnetic resonance (MR) imaging by using three approaches: a probabilistic neural network (PNN) approach, a semiautomated method that uses a bifeature space approach with operator intervention at each section, and the "gold standard" of manual outlining of lesions. Each patient underwent two MR studies in 1 day. RESULTS: The PNN approach allows reasonable quantitation of large data sets with minimal operator input. The mean intraobserver error for the PNN approach was competitive with the more time-consuming bifeature space approach (5.2% vs 4.4%, respectively). On average, both computer assisted methods performed better than the manual method (mean intraobserver error, 10.1%). CONCLUSION: The agreement between the two computerized quantitation approaches was good. The number of interactive steps was substantially reduced with the PNN technique, leading to minimal operator intervention time.

Brain↗
PubMed1995

Automated discrimination and quantification of idiopathic pulmonary fibrosis from normal lung parenchyma using generalized fractal dimensions in high-resolution computed tomography images.

RATIONALE AND OBJECTIVES: We computed generalized fractal dimensions for high-resolution computed tomography (HRCT) images to investigate their value in the discrimination and quantification of idiopathic pulmonary fibrosis (IPF) from normal lung parenchyma. METHODS: A probability distribution that was based on the pixel value in each image was used to compute capacity, information, and higher fractal dimensions for a series of 52 HRCT slices obtained from four patients. Qualitative classification of normal, mild, moderate, and severe IPF cases was achieved by computing the following parameter: DD = D0 - 2D1 + D2, where D0, D1, and D2 represents the capacity, information, and pair correlation dimensions, respectively. A multiple linear regression analysis using morphometric quantification for the set of 52 slices was tested for all possible combinations of the parameters D0, D1, D2, and D3. The generalizability of the model was tested by predicting the extent of IPF for each patient from a regression model computed with the remaining slices in the database. RESULTS: The best regression results were obtained using the independent parameters D1 and D2 to quantify the extent of diseased lung parenchyma. The technique was tested with 48 slices from 12 new patients. The results indicated that the extent of IPF could be predicted within the confidence limits given by the regression analysis. CONCLUSION: The extent of IPF can be predicted well within the 90% confidence interval given by the model. The width of the confidence interval decreases as the number of slices used in the linear regression model increases. This operator-independent quantitative technique may be useful in the follow-up of patients with IPF.

Confidence Intervals↗
PubMedDate not supplied

[Analog gamma camera digitalization computer system].

OBJECTIVE: Digitalization of analogue gamma cameras systems, using special acquisition boards in microcomputers and appropriate software for acquisition and processing of nuclear medicine images is described in detail. MATERIAL AND METHODS: Microcomputer integrated systems interconnected by means of a Local Area Network (LAN) and connected to several gamma cameras have been implemented using specialized acquisition boards. The PIP software (Portable Image Processing) was installed on each microcomputer to acquire and preprocess the nuclear medicine images. A specialized image processing software has been designed and developed for these purposes. This software allows processing of each nuclear medicine exam, in a semiautomatic procedure, and recording of the results on radiological films. RESULTS: . A stable, flexible and inexpensive system which makes it possible to digitize, visualize, process, and print nuclear medicine images obtained from analogue gamma cameras was implemented in the Nuclear Medicine Division. Such a system yields higher quality images than those obtained with analogue cameras while keeping operating costs considerably lower (filming: 24.6%, fixing 48.2% and developing 26%.) CONCLUSIONS: Analogue gamma camera systems can be digitalized economically. This system makes it possible to obtain optimal clinical quality nuclear medicine images, to increase the acquisition and processing efficiency, and to reduce the steps involved in each exam.

Computers↗