It is difficult for radiologists to classify pneumoconiosis from category 0 to category 3 on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on a three-stage artificial neural network (ANN) method for classification based on four texture features. The image database consists of 36 chest radiographs classified as category 0 to category 3. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from chest radiographs. We obtained a gray-level histogram, histogram of gray-level difference, gray-level run-length matrix (GLRLM) feature image, and gray-level co-occurrence matrix (GLCOM) feature image in each ROI. For ROI-based classification, the first ANN was trained with each texture feature. Next, the second ANN was trained with output patterns obtained from the first ANN. Finally, we obtained a case-based classification for distinguishing among four categories with the third ANN method. We determined the performance of the third ANN by receiver operating characteristic (ROC) analysis. The areas under the ROC curve (AUC) of the highest category (severe pneumoconiosis) case and the lowest category (early pneumoconiosis) case were 0.89 ± 0.09 and 0.84 ± 0.12, respectively. The three-stage ANN with four texture features showed the highest performance for classification among the four categories. Our CAD system would be useful for assisting radiologists in classification of pneumoconiosis from category 0 to category 3.

It is difficult for radiologists to classify pneumoconiosis with small nodules on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on the rule-based plus artificial neural network (ANN) method for distinction between normal and abnormal regions of interest (ROIs) selected from chest radiographs with and without pneumoconiosis. The image database consists of 11 normal and 12 abnormal chest radiographs. These abnormal cases included five silicoses, four asbestoses, and three other pneumoconioses. ROIs (matrix size, 32 × 32) were selected from normal and abnormal lungs. We obtained power spectra (PS) by Fourier transform for the frequency analysis. A rule-based method using PS values at 0.179 and 0.357 cycles per millimeter, corresponding to the spatial frequencies of nodular patterns, were employed for identification of obviously normal or obviously abnormal ROIs. Then, ANN was applied for classification of the remaining normal and abnormal ROIs, which were not classified as obviously abnormal or normal by the rule-based method. The classification performance was evaluated by the area under the receiver operating characteristic curve (Az value). The Az value was 0.972 ± 0.012 for the rule-based plus ANN method, which was larger than that of 0.961 ± 0.016 for the ANN method alone (P ≤ 0.15) and that of 0.873 for the rule-based method alone. We have developed a rule-based plus pattern recognition technique based on the ANN for classification of pneumoconiosis on chest radiography. Our CAD system based on PS would be useful to assist radiologists in the classification of pneumoconiosis.

We have been developing a computer-aided detection (CAD) scheme for pneumoconiosis based on a rule-based plus artificial neural network (ANN) analysis of power spectra. In this study, we have developed three enhancement methods for the abnormal patterns to reduce false-positive and false-negative values. The image database consisted of 2 normal and 15 abnormal chest radiographs. The International Labour Organization standard chest radiographs with pneumoconiosis were categorized as subcategory, size, and shape of pneumoconiosis. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from normal and abnormal lungs. Three new enhanced methods were obtained by window function, top-hat transformation, and gray-level co-occurrence matrix analysis. We calculated the power spectrum (PS) of all ROIs by Fourier transform. For the classification between normal and abnormal ROIs, we applied a combined analysis using the ruled-based plus the ANN method. To evaluate the overall performance of this CAD scheme, we employed ROC analysis for distinguishing between normal and abnormal ROIs. On the chest radiographs of the highest categories (severe pneumoconiosis) and the lowest categories (early pneumoconiosis), this CAD scheme achieved area under the curve (AUC) values of 0.93 ± 0.02 and 0.72 ± 0.03. The combined rule-based plus ANN method with the three new enhanced methods obtained the highest classification performance for distinguishing between abnormal and normal ROIs. Our CAD system based on the three new enhanced methods would be useful in assisting radiologists in the classification of pneumoconiosis.

Accurate registration of the corresponding non-enhanced and arterial-phase CT images is necessary to create temporal and dynamic subtraction images for the enhancement of subtle abnormalities. However, respiratory movement causes misregistration at the periphery of the liver. To reduce these misregistration errors, we developed a temporal and dynamic subtraction technique to enhance small HCC by 3D global matching and nonlinear image warping techniques. The study population consisted of 21 patients with HCC. Using the 3D global matching and nonlinear image warping technique, we registered current and previous arterial-phase CT images or current non-enhanced and arterial-phase CT images obtained in the same position. The temporal subtraction image was obtained by subtracting the previous arterial-phase CT image from the warped current arterial-phase CT image. The dynamic subtraction image was obtained by the subtraction of the current non-enhanced CT image from the warped current arterial-phase CT image. The percentage of fair or superior temporal subtraction images increased from 52.4% to 95.2% using the new technique, while on the dynamic subtraction images, the percentage increased from 66.6% to 95.2%. The new subtraction technique may facilitate the diagnosis of subtle HCC based on the superior ability of these subtraction images to show nodular and/or ring enhancement.

22It is often difficult for radiologists to identify small hepatocellular carcinoma (HCC) due to 23 insufficient contrast enhancement. Therefore, we have developed a new computer-aided 24 temporal and dynamic subtraction technique to enhance small HCC, after automatically 25 selecting images set at the same anatomical position from the present (non-enhanced and 26 arterial-phase CT images) and previous images. The present study was performed with CT 27 images from fourteen subjects. First, we used template-matching based on similarities in liver 28 2 shape between the present (non-enhanced and arterial-phase CT images) and previous 1 arterial-phase CT images at the same position. Temporal subtraction images were then obtained 2 by subtraction of the previous image from the present image taken at the same position of the 3 liver, and dynamic subtraction images were also obtained by subtraction of non-enhanced CT 4 images from arterial-phase CT images taken at the same position of the liver. Twenty-one of 22 5 nodules (95.5%) with contrast enhancement were visualised in temporal and dynamic 6 subtraction images. Increases of 150% and 140% in nodule-to-liver contrast were observed on 7 dynamic and temporal subtraction images compared with present arterial-phase CT images, 8respectively. These subtraction images may be useful as reference images in detection of small 9 moderately differentiated HCC. 10 11