Computer-assisted Diagnosis Scheme Research Articles

Physics and engineering aspects of cell and tissue imaging systems: microscopic devices and computer assisted diagnosis.

The conventional optical microscopes have been used widely in scientific research and in clinical practice. The modern digital microscopic devices combine the power of optical imaging and computerized analysis, archiving and communication techniques. It has a great potential in pathological examinations for improving the efficiency and accuracy of clinical diagnosis. This chapter reviews the basic optical principles of conventional microscopes, fluorescence microscopes and electron microscopes. The recent developments and future clinical applications of advanced digital microscopic imaging methods and computer assisted diagnosis schemes are also discussed.

Automated Detection of Polyps with CT Colonography: Evaluation of Volumetric Features for Reduction of False-Positive Findings

Rationale and Objectives To achieve high performance in computer-assisted diagnosis (CAD) of polyps with computed tomographic (CT) colonography, the authors ( a) developed new gradient concentration and directional gradient concentration (DGC) features for differentiating between the true-positive and false-positive (FP) findings generated by the authors' CAD scheme, and ( b) used receiver operating characteristic (ROC) analysis to quantify the differentiation performance of these and other volumetric features. Materials and Methods CT colonography was performed in 43 patients prone and supine with a helical CT scanner; there were 12 polyps in 11 patients. The polyp candidates generated by the authors' CAD scheme were characterized by nine statistics of six volumetric features, and the resulting 54 feature statistics were combined by a linear or quadratic discriminant classifier. The discrimination performance was measured with round-robin method by ROC analysis and the FP rate of the CAD scheme. Results The mean value of shape index (SI) yielded the highest individual ROC performance (area under the curve = 0.92). Among combinations, the mean values of SI and DGC and the variance of CT value yielded a high ROC performance (area under the curve = 0.95). With quadratic classifier, the sensitivity and FP rate of the case–based (data set–based) analysis was 100% (95%) with 2.4 FP findings per patient (1.7 FP findings per data set), respectively. Conclusion Combination of the mean values of SI and DGC and the variance of CT value reduced the FP rate substantially without sacrificing sensitivity. These three features are potentially useful in improving the performance of the authors' CAD scheme for detecting polypswith CT colonography.

Incorporation of a set enumeration trees-based classifier into a hybrid computer-assisted diagnosis scheme for mass detection

The authors evaluated whether a hybrid classifier of two independent computer-aided diagnosis (CAD) schemes, the set enumeration (SE) trees approach and an artificial neural network (ANN), could improve the detection of masses on digitized mammograms. The potential benefits resulting from the interpretability of the SE trees model was also explored. Two hundred thirty verified mass regions and 230 negative but suspicious regions were randomly selected from 618 digitized mammograms. Each region was represented by a 24-parameter feature vector. These features were used as input data for the SE trees and ANN-based schemes. After the positive and negative regions were randomly segmented into five exclusive partitions, a fivefold cross-validation method was applied to evaluate and compare the performance of the SE trees, ANN, and hybrid system in the identification of masses. The performance of the SE trees approach was comparable to that of the ANN. The average area under the receiver operating characteristic (ROC) curves for all five partitions was 0.88 (standard deviation, 0.04). Owing to the relatively low correlation between the region-based results of the SE trees and ANN methods, the hybrid classifier yielded a significantly improved performance, with an area under the ROC curve of 0.94 (standard deviation, 0.02; P < .05). The hybrid CAD scheme significantly improved performance. The amenability of the SE trees models to interpretation may aid in the assessment of the importance of specific features.

Adequacy testing of training set sample sizes in the development of a computer-assisted diagnosis scheme.

The authors assessed the performance changes of a computer-assisted diagnosis (CAD) scheme as a function of the number of regions used for training (rule-setting). One hundred twenty regions depicting actual masses and 400 suspicious but actually negative regions were selected as a testing data set from a database of 2,146 regions identified as suspicious on 618 mammograms. An artificial neural network using 24 and 16 region-based features as input neurons was applied to classify the regions as positive or negative for the presence of a mass. CAD scheme performance was evaluated on the testing data set as the number of regions used for training increased from 60 to 496. As the number of regions in the training sets increased, the results decreased and plateaued beyond a sample size of approximately 200 regions. Performance with the testing data set continued to improve as the training data set increased in size. A trend in a system's performance as a function of training set size can be used to assess adequacy of the training data set in the development of a CAD scheme.

Mass detection in digitized mammograms using two independent computer-assisted diagnosis schemes.

Using two independent computer-assisted diagnosis (CAD) schemes, we investigated the potential to improve the sensitivity of mass detection by applying a logical "or" operation and to improve the specificity using a logical "and" operation. Two independent mass detectors, one with Gaussian bandpass filtering and multilayer topographic feature analysis and the other with a five-stage search for a single suspicious region, were applied to a large image database that included 428 digitized mammograms with 220 verified masses. The performance of the two schemes and a combination of them in the form of either logical "or" or logical "and" operations were compared. In this preliminary study, a multilayer topographic feature analysis CAD scheme (CAD-1) achieved a sensitivity of 96% and had a false-positive detection rate of 0.79 per image. A five-stage search method scheme (CAD-2) achieved a sensitivity of 94% and had a false-positive detection rate of 1.69 per image. With an "or" operation, the combined results yielded 100% sensitivity with a false-positive detection rate of 2.07 per image. A logical "and" operation produced a reduction of the false-positive detection rate to 0.4 per image, but sensitivity also decreased to 90%. Similar to an independent double-reading approach and depending upon the relevant clinical question, sensitivity or specificity can be improved by combining the results of several independent CAD schemes.