Biomedical subjects

Shih-Chung B Lo

Publications and source records attributed to Shih-Chung B Lo.

4 recordsLinked to original sources

PubMed2004

Sensitivity of noncommercial computer-aided detection system for mammographic breast cancer detection: pilot clinical trial.

PURPOSE: To evaluate a noncommercial computer-aided detection (CAD) program for breast cancer detection with screening mammography. MATERIALS AND METHODS: A CAD program was developed for mammographic breast cancer detection. The program was applied to 2,389 patients' screening mammograms at two geographically remote academic institutions (institutions A and B). Thirteen radiologists who specialized in breast imaging participated in this pilot study. For each case, the individual radiologist performed a prospective Breast Imaging Reporting and Data System (BI-RADS) assessment after viewing of the screening mammogram. Subsequently, the radiologist was shown CAD results and rendered a second BI-RADS assessment by using knowledge of both mammographic appearance and CAD results. Outcome analysis of results of examination in patients recalled for a repeat examination, of biopsy, and of 1-year follow-up examination was recorded. Correct detection with CAD included a computer-generated mark indicating a possible malignancy on craniocaudal or mediolateral oblique views or both. RESULTS: Eleven (0.46%) of 2,389 patients had mammographically detected nonpalpable breast cancers. Ten (91%) of 11 (95% CI: 74%, 100%) cancers were correctly identified with CAD. Radiologist sensitivity without CAD was 91% (10 of 11; 95% CI: 74%, 100%). In 1,077 patients, follow-up findings were documented at 1 year. Five (0.46%) patients developed cancers, which were found on subsequent screening mammograms. The area where the cancers developed in two (40%) of these five patients was marked (true-positive finding) by the computer in the preceding year. Because of CAD results, a 9.7% increase in recall rate from 14.4% (344 of 2,389) to 15.8% (378 of 2,389) occurred. Radiologists' recall rate of study patients prior to use of CAD was 31% higher than the average rate for nonstudy cases (10.3%) during the same time period at institution A. CONCLUSION: Performance of the CAD program had a very high sensitivity of 91% (95% CI: 74%, 100%).

Adult↗

PubMed2004

Steepest changes of a probability-based cost function for delineation of mammographic masses: a validation study.

Our purpose in this work was to develop an automatic boundary detection method for mammographic masses and to rigorously test this method via statistical analysis. The segmentation method utilized a steepest change analysis technique for determining the mass boundaries based on a composed probability density cost function. Previous investigators have shown that this function can be utilized to determine the border of the mass body. We have further analyzed this method and have discovered that the steepest changes in this function can produce mass delineations that include extended projections. The method was tested on 124 digitized mammograms selected from the University of South Florida's Digital Database for Screening Mammography (DDSM). The segmentation results were validated using overlap, accuracy, sensitivity, and specificity statistics, where the gold standards were manual traces provided by two expert radiologists. We have concluded that the best intensity threshold corresponds to a particular steepest change location within the composed probability density function. We also found that our results are more closely correlated with one expert than with the second expert. These findings were verified via Analysis of Variance (ANOVA) testing. The ANOVA tests obtained p-values ranging from 1.03 x 10(-2)-7.51 x 10(-17) for the single observer studies and 2.03 x 10(-2)-9.43 x 10(-4) for the two observer studies. Results were categorized using three significance levels, i.e., p<0.001 (extremely significant), p <0.01 (very significant), and p <0.05 (significant), respectively.

Algorithms↗

PubMed2003

Optimization of wavelet decomposition for image compression and feature preservation.

A neural-network-based framework has been developed to search for an optimal wavelet kernel that can be used for a specific image processing task. In this paper, a linear convolution neural network was employed to seek a wavelet that minimizes errors and maximizes compression efficiency for an image or a defined image pattern such as microcalcifications in mammograms and bone in computed tomography (CT) head images. We have used this method to evaluate the performance of tap-4 wavelets on mammograms, CTs, magnetic resonance images, and Lena images. We found that the Daubechies wavelet or those wavelets with similar filtering characteristics can produce the highest compression efficiency with the smallest mean-square-error for many image patterns including general image textures as well as microcalcifications in digital mammograms. However, the Haar wavelet produces the best results on sharp edges and low-noise smooth areas. We also found that a special wavelet whose low-pass filter coefficients are 0.32252136, 0.85258927, 1.38458542, and -0.14548269) produces the best preservation outcomes in all tested microcalcification features including the peak signal-to-noise ratio, the contrast and the figure of merit in the wavelet lossy compression scheme. Having analyzed the spectrum of the wavelet filters, we can find the compression outcomes and feature preservation characteristics as a function of wavelets. This newly developed optimization approach can be generalized to other image analysis applications where a wavelet decomposition is employed.

Algorithms↗

PubMed2002

A multiple circular path convolution neural network system for detection of mammographic masses.

A multiple circular path convolution neural network (MCPCNN) architecture specifically designed for the analysis of tumor and tumor-like structures has been constructed. We first divided each suspected tumor area into sectors and computed the defined mass features for each sector independently. These sector features were used on the input layer and were coordinated by convolution kernels of different sizes that propagated signals to the second layer in the neural network system. The convolution kernels were trained, as required, by presenting the training cases to the neural network. In this study, randomly selected mammograms were processed by a dual morphological enhancement technique. Radiodense areas were isolated and were delineated using a region growing algorithm. The boundary of each region of interest was then divided into 36 sectors using 36 equi-angular dividers radiated from the center of the region. A total of 144 Breast Imaging-Reporting and Data System-based features (i.e., four features per sector for 36 sectors) were computed as input values for the evaluation of this newly invented neural network system. The overall performance was 0.78-0.80 for the areas (Az) under the receiver operating characteristic curves using the conventional feed-forward neural network in the detection of mammographic masses. The performance was markedly improved with Az values ranging from 0.84 to 0.89 using the MCPCNN. This paper does not intend to claim the best mass detection system. Instead it reports a potentially better neural network structure for analyzing a set of the mass features defined by an investigator.

Breast Neoplasms↗