Biomedical subjects

V Chalana

Publications and source records attributed to V Chalana.

5 recordsLinked to original sources

PubMed1998

Pubic arch detection in transrectal ultrasound guided prostate cancer therapy.

New biopsy techniques, increased life expectancy, and prostate-specific antigen (PSA) screening have contributed to an increase in the reported incidence of prostate cancer. Among several treatment options available to the patients, transperineal prostate brachytherapy has emerged as a medically successful, cost-effective outpatient procedure for treating localized prostate cancer. Transperineal prostate brachytherapy employs transrectal ultrasound (TRUS) as the primary imaging modality to accurately preplan and subsequently execute the placement of radioactive seeds into the prostate. Under TRUS guidance, a needle (preloaded with radioactive seeds) is inserted through a template guide, through the perineum and into a predetermined prostate target. The pubic arch, formed by the central union of pelvic bones, is a potential barrier to the passage of these needles in the prostate. A critical aspect, therefore, in the planning and execution of the brachytherapy procedure is the accurate assessment of pubic arch interference (PAI) in relation to the prostate. Traditionally, the evaluation of PAI has involved computed tomography correlate scanning or crude subjective evaluations. In this paper, we describe a new method of assessing PAI by detecting the pubic arch via image processing on the TRUS images. The PAI detection (PAID) algorithm first uses a technique known as sticks to selectively enhance the contrast of linear features in ultrasound images. Next, the enhanced image is thresholded via percentile thresholding. Finally, we fit a parabola (a model for the pubic arch) recursively to the thresholded image. Our evaluation result from 15 cases indicates that the algorithm can successfully detect the pubic arch with 90% accuracy. Based on this study, we believe that detecting the pubic arch and assessing PAI can be done practically and more accurately in the clinical setting using TRUS rather than the current available methods.

Brachytherapy↗

PubMed1997

Interactive automatic fetal head measurements from ultrasound images using multimedia computer technology.

We have developed a tool to automatically detect inner and outer skull boundaries of a fetal head in ultrasound images. These boundaries are used to measure biparietal diameter (BPD) and head circumference (HC). The algorithm is based on active contour models and takes 32 s on a Sun SparcStation 20/71. A high-performance desktop multimedia system called MediaStation 5000 (MS5000) is used as a model for our future ultrasound subsystem. On the MS5000, the optimized implementation of this algorithm takes 248 ms. The difference (between the computer-measured values on MS5000 and the gold standard) for BPD and HC was 1.43% (sigma = 1.00%) and 1.96% (sigma = 1.96%), respectively. According to our data analysis, no significant differences exist in the BPD and HC measurements made on the MS5000 and those measurements made on the Sun SparcStation 20/71. Reduction in the overall execution time from 32 s to 248 ms will help making this algorithm a practical ultrasound tool for sonographers.

Algorithms↗

PubMed1997

A methodology for evaluation of boundary detection algorithms on medical images.

Image segmentation is the partition of an image into a set of nonoverlapping regions whose union is the entire image. The image is decomposed into meaningful parts which are uniform with respect to certain characteristics, such as gray level or texture. In this paper, we propose a methodology for evaluating medical image segmentation algorithms wherein the only information available is boundaries outlined by multiple expert observers. In this case, the results of the segmentation algorithm can be evaluated against the multiple observers' outlines. We have derived statistics to enable us to find whether the computer-generated boundaries agree with the observers' hand-outlined boundaries as much as the different observers agree with each other. We illustrate the use of this methodology by evaluating image segmentation algorithms on two different applications in ultrasound imaging. In the first application, we attempt to find the epicardial and endocardial boundaries from cardiac ultrasound images, and in the second application, our goal is to find the fetal skull and abdomen boundaries from prenatal ultrasound images.

Abdomen↗

PubMed1996

Automatic fetal head measurements from sonographic images.

RATIONALE AND OBJECTIVES: We designed an image processing technique to automatically measure the biparietal diameter (BPD) and head circumference (HC) from prenatal sonograms. We evaluated the performance of the algorithm by comparing the resulting measurements with those made by experienced sonographers. METHODS: Thirty-five digitized sonograms of the fetal head were obtained during routine imaging. The BPD and HC were automatically computed by detecting the inner and outer boundaries of the fetal skull using the computer vision technique known as the "active contour model." Six experienced sonographers also measured the BPD and HC on these images. RESULTS: The algorithm failed to locate the boundaries in two of the 35 cases. For the remaining cases, the mean absolute difference between the automated measurements and the average of the six observers was 1.4% for BPD and 2.9% for HC. The correlations were .999 for the BPD and .994 for the HC. The computer's measurements were no different from the six observers' measurements than the observers' measurements were from one another. CONCLUSION: The tested algorithm effectively and accurately measures BPD and HC automatically. We are currently in the process of integrating this algorithm into an ultrasound machine.

Algorithms↗

PubMed1996

A real-time scan conversion algorithm on commercially available microprocessors.

We have developed a new ultrasound scan conversion algorithm that can be executed very efficiently on modern microprocessors. Our algorithm is designed to handle the address calculations and input and output (I/O) data loading concurrently with the interpolation. The processing unit's computing power can be dedicated to performing pixel interpolations while the other operations are handled by an independent direct memory access (DMA) controller. By making intelligent use of the I/O transfer capabilities of the DMA controller, the algorithm avoids spending the processing unit's valuable computing cycles in address calculations and nonactive pixel blanking. Furthermore, the new approach speeds up the computation by utilizing the ability of superscalar and very long instruction word (VLIW) processors to perform multiple operations in parallel. Our scan conversion algorithm was implemented on a multimedia and imaging system based on the Texas Instruments TMS320C80 Multimedia Video Processor (MVP). Computing cycles are spent only on predeterminable nonzero output pixels. For example, an execution time of 11.4 ms was achieved when there are 101,829 nonzero output pixels. This algorithm demonstrates a substantial improvement over previous scan conversion algorithms, and its optimized implementation enables modern commercially available programmable processors to support scan conversion at video rates.

Algorithms↗