Automated identification of single and clustered chromosomes for metaphase image analysis.

Background: Chromosome analysis is laborious and time-consuming. Automated methods can significantly increase the efficiency of chromosome analysis. For the automated analysis of chromosome images, single and clustered chromosomes must be identified. Herein, we propose a feature-based method for distinguishing between single chromosomes and clustered chromosome. Method: The proposed method comprises three main steps. In the first step, chromosome objects are segmented from metaphase chromosome images in advance. In the second step, seven features are extracted from each segmented object, i.e., the normalized area, area/boundary ratio, side branch index, exhaustive thresholding index, normalized minimum width, minimum concave angle, and maximum boundary shift. Finally, the segmented objects are classified as a single chromosome or chromosome cluster using a combination of the seven features. Results: In total, 43,391 segmented objects, including 39,892 single chromosomes and 3,499 chromosome clusters, are used to evaluate the proposed method. The results show that the proposed method achieves an accuracy of 98.92% by combining the seven features using support vector machine. Conclusions: The proposed method is highly effective in distinguishing between single and clustered chromosomes and can be used as a preprocessing procedure for automated chromosome image analysis.

Fully automated determination of arch angle on weight-bearing foot radiograph.

BACKGROUND AND OBJECTIVE: Flatfeet can be evaluated by measuring the calcaneal-fifth metatarsal angle on a weight-bearing lateral foot radiograph. This study aimed to develop an automated method for determining the calcaneal-fifth metatarsal angle on weight-bearing lateral foot radiograph. METHOD: The proposed method comprises four processing steps: (1) identification of the regions including the calcaneus and fifth metatarsal bones in a foot image; (2) delineation of the contours of the calcaneus and the fifth metatarsal; (3) determination of the tangential lines of the two bones from the contours; and (4) determination of the calcaneal-fifth metatarsal angle between the two tangential lines as arch angle. RESULTS: The proposed method was evaluated using 300 weight-bearing lateral foot radiographs. The arch angles determined by the proposed method were compared with those measured by a radiologist, and the errors between the automatically and manually determined angles were used to evaluate the precision of the method. The average error in the proposed method was found to be 1.12°â€¯±â€¯1.57° In the study, in 73.33% of the cases, the arch angles could be determined automatically without redrawing any tangential lines; in 23.00% of the cases, the angles would be correctly determined by redrawing one of the tangential lines; further, in only 3.67% of the cases, both the calcaneal and fifth metatarsal tangential lines needed to be redrawn to determine the arch angles. CONCLUSION: The results revealed that the proposed method has potential for assisting doctors in measuring the arch angles on weight-bearing lateral foot radiographs more efficiently.

Comparisons of Reproducibility and Mean Values of Diffusion Tensor Imaging-Derived Indices between Unipolar and Bipolar Diffusion Pulse Sequences.

Eddy current distortion is an important issue that may influence the quantitative measurements of diffusion tensor imaging (DTI). The corrections of eddy current artifacts could be performed using bipolar diffusion gradients or unipolar gradients with affine registration. Whether the diffusion pulse sequence affects the quantification of DTI indices and the technique that produces more reliable DTI indices in terms of reproducibility both remain unclear. Therefore, the purpose of this study was to compare the reproducibility and mean values of DTI-derived indices between unipolar and bipolar diffusion pulse sequences based on actual human brain data. Five repeated datasets of unipolar and bipolar DTI were acquired from 10 healthy subjects at different echo times (TEs). The reproducibility and mean values of DTI indices were assessed by calculating the coefficient of variation and mean values of the 5 repeated measurements. The results revealed that the reproducibility and mean values of DTI indices were significantly affected by the pulse sequence. Unipolar DTI exhibited significantly higher reproducibility than bipolar DTI even at the same TE, and the mean values of DTI indices were significantly different between them. Therefore, we concluded that the reproducibility and mean values of DTI indices were significantly influenced by diffusion pulse sequences.

Automated detection of endotracheal tubes in paediatric chest radiographs.

The aim of this study was to develop an automated method for the detection of endotracheal tube and location of its tip in paediatric chest radiographs. In this method, a seed point was first determined from the line crossing the cervical region and a line path was traced from the seed point. Two features, Lmax and C, were determined from the path and were combined to detect the existence of the endotracheal tube. Multiple thresholds applied to the line path were used to determine the candidate locations for the tip, and the most suitable location was selected from these candidates by analysing the image features. To evaluate the performance of detection of endotracheal tube existence, support vector machine was used to classify the images with and without endotracheal tubes on the basis of Lmax and C. The discriminant performance of the method was evaluated using receiver operating characteristic (ROC) analysis. To evaluate the precision of the detected tip locations, the tip locations in paediatric chest images were annotated by a radiologist. The distance (error) between the detected and annotated locations was used to evaluate detection precision for the tip location. The proposed method was evaluated using 528 images with endotracheal tubes and 816 images without endotracheal tubes. The discriminant performance in this study, evaluated as Az (area under the ROC curve), for detecting the existence of endotracheal tubes on the basis of the two features was 0.943±0.009, and the detection error of the tip location was 1.89±2.01mm. The proposed method obtained high performance results and could be useful for detecting the malposition of endotracheal tubes in paediatric chest radiographs.

Computer-aided detection system for chest radiography: reducing report turnaround times of examinations with abnormalities.

BACKGROUND: The ability to give high priority to examinations with pathological findings could be very useful to radiologists with large work lists who wish to first evaluate the most critical studies. A computer-aided detection (CAD) system for identifying chest examinations with abnormalities has therefore been developed. PURPOSE: To evaluate the effectiveness of a CAD system on report turnaround times of chest examinations with abnormalities. MATERIAL AND METHODS: The CAD system was designed to automatically mark chest examinations with possible abnormalities in the work list of radiologists interpreting chest examinations. The system evaluation was performed in two phases: two radiologists interpreted the chest examinations without CAD in phase 1 and with CAD in phase 2. The time information recorded by the radiology information system was then used to calculate the turnaround times. All chest examinations were reviewed by two other radiologists and were divided into normal and abnormal groups. The turnaround times for the examinations with pathological findings with and without the CAD system assistance were compared. RESULTS: The sensitivity and specificity of the CAD for chest abnormalities were 0.790 and 0.697, respectively, and use of the CAD system decreased the turnaround time for chest examinations with abnormalities by 44%. CONCLUSION: The turnaround times required for radiologists to identify chest examinations with abnormalities could be reduced by using the CAD system. This system could be useful for radiologists with large work lists who wish to first evaluate the most critical studies.

Automated detection of fovea in fundus images based on vessel-free zone and adaptive Gaussian template.

This study developed a computerised method for fovea centre detection in fundus images. In the method, the centre of the optic disc was localised first by the template matching method, the disc-fovea axis (a line connecting the optic disc centre and the fovea) was then determined by searching the vessel-free region, and finally the fovea centre was detected by matching the fovea template around the centre of the axis. Adaptive Gaussian templates were used to localise the centres of the optic disc and fovea for the images with different resolutions. The proposed method was evaluated using three publicly available databases (DIARETDB0, DIARETDB1 and MESSIDOR), which consisted of a total of 1419 fundus images with different resolutions. The proposed method obtained the fovea detection accuracies of 93.1%, 92.1% and 97.8% for the DIARETDB0, DIARETDB1 and MESSIDOR databases, respectively. The overall accuracy of the proposed method was 97.0% in this study.

Discovering hidden connections among diseases, genes and drugs based on microarray expression profiles with negative-term filtering.

Microarrays based on gene expression profiles (GEPs) can be tailored specifically for a variety of topics to provide a precise and efficient means with which to discover hidden information. This study proposes a novel means of employing existing GEPs to reveal hidden relationships among diseases, genes, and drugs within a rich biomedical database, PubMed. Unlike the co-occurrence method, which considers only the appearance of keywords, the proposed method also takes into account negative relationships and non-relationships among keywords, the importance of which has been demonstrated in previous studies. Three scenarios were conducted to verify the efficacy of the proposed method. In Scenario 1, disease and drug GEPs (disease: lymphoma cancer, lymph node cancer, and drug: cyclophosphamide) were used to obtain lists of disease- and drug-related genes. Fifteen hidden connections were identified between the diseases and the drug. In Scenario 2, we adopted different diseases and drug GEPs (disease: AML-ALL dataset and drug: Gefitinib) to obtain lists of important diseases and drug-related genes. In this case, ten hidden connections were identified. In Scenario 3, we obtained a list of disease-related genes from the disease-related GEP (liver cancer) and the drug (Capecitabine) on the PharmGKB website, resulting in twenty-two hidden connections. Experimental results demonstrate the efficacy of the proposed method in uncovering hidden connections among diseases, genes, and drugs. Following implementation of the weight function in the proposed method, a large number of the documents obtained in each of the scenarios were judged to be related: 834 of 4028 documents, 789 of 1216 documents, and 1928 of 3791 documents in Scenarios 1, 2, and 3, respectively. The negative-term filtering scheme also uncovered a large number of negative relationships as well as non-relationships among these connections: 97 of 834, 38 of 789, and 202 of 1928 in Scenarios 1, 2, and 3, respectively.

Automated patient identity recognition by analysis of chest radiograph features.

RATIONALE AND OBJECTIVES: The aim of this study was to develop a computerized scheme for automated identity recognition based on chest radiograph features. MATERIALS AND METHODS: The proposed method was evaluated on a database consisting of 1000 pairs of posteroanterior chest radiographs. The method was based on six features: length of the lung field, size of the heart, area of the body, and widths of the upper, middle, and lower thoracic cage. The values for the six features were determined from a chest image, and absolute differences in feature values between the two images (feature errors) were used as indices of image similarity. The performance of the proposed method was evaluated by receiver operating characteristic (ROC) analysis. The discriminant performance was evaluated as the area Az under the ROC curve. RESULTS: The discriminant performance Az of the feature errors for lung field length, heart size, body area, upper cage width, middle cage width, and lower cage width were 0.794 ± 0.005, 0.737 ± 0.007, 0.820 ± 0.008, 0.860 ± 0.005, 0.894 ± 0.006, and 0.873 ± 0.006, respectively. The combination of the six feature errors obtained an Az value of 0.963 ± 0.002. CONCLUSION: The results indicate that combining the six features yields a high discriminant performance in recognizing patient identity. The method has potential usefulness for automated identity recognition to ensure that chest radiographs are associated with the correct patient.

A computerized method for automated identification of erect posteroanterior and supine anteroposterior chest radiographs.

A computerized scheme was developed for automated identification of erect posteroanterior (PA) and supine anteroposterior (AP) chest radiographs. The method was based on three features, the tilt angle of the scapula superior border, the tilt angle of the clavicle and the extent of radiolucence in lung fields, to identify the view of a chest radiograph. The three indices A(scapula), A(clavicle) and C(lung) were determined from a chest image for the three features. Linear discriminant analysis was used to classify PA and AP chest images based on the three indices. The performance of the method was evaluated by receiver operating characteristic analysis. The proposed method was evaluated using a database of 600 PA and 600 AP chest radiographs. The discriminant performances Az of A(scapula), A(clavicle) and C(lung) were 0.878 ± 0.010, 0.683 ± 0.015 and 0.962 ± 0.006, respectively. The combination of the three indices obtained an Az value of 0.979 ± 0.004. The results indicate that the combination of the three indices could yield high discriminant performance. The proposed method could provide radiologists with information about the view of chest radiographs for interpretation or could be used as a preprocessing step for analyzing chest images.

Zone-based analysis for automated detection of abnormalities in chest radiographs.

PURPOSE: The aim of this study was to develop an automated method for detection of local texture-based and density-based abnormalities in chest radiographs. METHODS: The method was based on profile analysis to detect abnormalities in chest radiographs. In the method, one density-based feature, Density Symmetry Index, and two texture-based features, Roughness Maximum Index and Roughness Symmetry Index, were used to detect abnormalities in the lung fields. In each chest radiograph, the lung fields were divided into four zones initially and then the method was applied to each zone separately. For each zone, Density Symmetry Index was obtained from the projection profile of each zone, and Roughness Maximum Index and Roughness Symmetry Index were obtained by measuring the roughness of the horizontal profiles via moving average technique. Linear discriminant analysis was used to classify normal and abnormal cases based on the three indices. The discriminant performance of the method was evaluated using ROC analysis. RESULTS: The method was evaluated on a database of 250 normal and 250 abnormal chest images. In the optimized conditions, the zone-based performance Az of the method for zones 1, 2, 3, and 4 were 0.917, 0.897, 0.892, and 0.814, respectively, and the case-based performance Az of the method was 0.842. Our previous method for detection of gross abnormalities was also evaluated on the same database. The case-based performance of our previous method was 0.689. CONCLUSIONS: In comparing the previous method and the new method proposed in this study, there was a great improvement by the new method for detection of local texture-based and density-based abnormalities. The new method combined with the previous one has potential for screening abnormalities in chest radiographs.

Projection profile analysis for identifying different views of chest radiographs.

RATIONALE AND OBJECTIVES: For computerized analysis of chest images in the clinical environment, identification of frontal (posteroanterior/anteroposterior) and lateral chest radiographs is an important preprocessing step. In this study, we developed a method to distinguish frontal from lateral views of the chest radiographs based on an analysis of the projection profile. MATERIALS AND METHODS: Projection profile is obtained by projecting a chest image on to the mediolateral axis. Two indices, body symmetry index and background percentage index, are computed from the projection profile. The combination of body symmetry index and background percentage index is used to determine the view of chest radiographs. The method is evaluated on a sample of 2000 frontal and 1000 lateral chest images. RESULTS: The values of body symmetry index are found to be 1.18 +/- 0.23 and 3.07 +/- 1.42 for frontal and lateral chest images, respectively. The values of background percentage index are found to be 0.03 +/- 0.05 and 0.33 +/- 0.09 for frontal and lateral chest images, respectively. The discrimination is evaluated by linear discriminant analysis and receiver operating characteristic analysis. Area Az under the receiver operating characteristic curve with the combination of the two indices is 0.993. CONCLUSION: The method can be used as a preprocessing step for further analysis in chest radiographs.

Projection profile analysis for automated detection of abnormalities in chest radiographs.

Abnormalities in chest images often present as abnormal opacity or abnormal asymmetry. We have developed a novel method for automated detection of abnormalities in chest radiographs by use of these features. Our method is based on an analysis of the projection profile obtained by projecting the pixels data of a frontal chest image on to the mediolateral axis. Two indices, lung opacity index and lung symmetry index, are computed from the projection profile. Lung opacity index and lung symmetry index are then combined to detect gross abnormalities in chest radiographs. The values of lung opacity index are found to be 0.38 +/- 0.05 and 0.37 +/- 0.06 for normal right and left lung, respectively. The values of lung symmetry index are found to be 0.018 +/- 0.014 for normal chest images. The discrimination for the combination of the two indices is evaluated by linear discriminant analysis and receiver operating characteristic (ROC) analysis. Area Az under the ROC curve with the combination of the two indices in the classification of normal and abnormal chest images is 0.963.