To identify asymptomatic patients is the challenging task and the essential first step in diagnosis. Findings of dental
panoramic radiographs include not only dental conditions but also radiographic signs that are suggestive of possible
systemic diseases such as osteoporosis, arteriosclerosis, and maxillary sinusitis. Detection of such signs on panoramic
radiographs has a potential to provide supplemental benefits for patients. However, it is not easy for general dental
practitioners to pay careful attention to such signs. We addressed the development of a computer-aided detection (CAD)
system that detects radiographic signs of pathology on panoramic images, and the design of the framework of new
screening pathway by cooperation of dentists and our CAD system. The performance evaluation of our CAD system
showed the sensitivity and specificity in the identification of osteoporotic patients were 92.6 % and 100 %, respectively,
and those of the maxillary sinus abnormality were 89.6 % and 73.6 %, respectively. The detection rate of carotid artery
calcifications that suggests the need for further medical evaluation was approximately 93.6 % with 4.4 false-positives per
image. To validate the utility of the new screening pathway, preliminary clinical trials by using our CAD system were
conducted. To date, 223 panoramic images were processed and 4 asymptomatic patients with suspected osteoporosis, 7
asymptomatic patients with suspected calcifications, and 40 asymptomatic patients with suspected maxillary sinusitis
were detected in our initial trial. It was suggested that our new screening pathway could be useful to identify
asymptomatic patients with systemic diseases.
Retinal nerve fiber layer defect (NFLD) is a major sign of glaucoma, which is the second leading cause of blindness in the world. Early detection of NFLDs is critical for improved prognosis of this progressive, blinding disease. We have investigated a computerized scheme for detection of NFLDs on retinal fundus images. In this study, 162 images, including 81 images with 99 NFLDs, were used. After major blood vessels were removed, the images were transformed so that the curved paths of retinal nerves become approximately straight on the basis of ellipses, and the Gabor filters were applied for enhancement of NFLDs. Bandlike regions darker than the surrounding pixels were detected as candidates of NFLDs. For each candidate, image features were determined and the likelihood of a true NFLD was determined by using the linear discriminant analysis and an artificial neural network (ANN). The sensitivity for detecting the NFLDs was 91% at 1.0 false positive per image by using the ANN. The proposed computerized system for the detection of NFLDs can be useful to physicians in the diagnosis of glaucoma in a mass screening.
Depth analysis of the optic nerve head (ONH) in the retinal fundus is important for the early detection of glaucoma. In this study, we investigate an automatic reconstruction method for the quantitative depth measurement of the ONH from a stereo retinal fundus image pair. We propose a technique to obtain the depth value from the stereo retinal fundus image pair, which mainly consists of five steps: 1. cutout of the ONH region from the stereo retinal fundus image pair, 2. registration of the stereo image pair, 3. disparity measurement, 4. noise reduction, and 5. quantitative depth calculation. Depth measurements of 12 normal eyes are performed using the stereo fundus camera and the Heidelberg Retina Tomograph (HRT), which is a confocal laser-scanning microscope. The depth values of the ONH obtained from the stereo retinal fundus image pair were in good accordance with the value obtained using HRT (r=0.80±0.15). These results indicate that our proposed method could be a useful and easy-to-handle tool for assessing the cup depth of the ONH in routine diagnosis as well as in glaucoma screening.
We have been developing several automated methods for detecting abnormalities in fundus images. The purpose of this
study is to improve our automated hemorrhage detection method to help diagnose diabetic retinopathy. We propose a
new method for preprocessing and false positive elimination in the present study. The brightness of the fundus image
was changed by the nonlinear curve with brightness values of the hue saturation value (HSV) space. In order to
emphasize brown regions, gamma correction was performed on each red, green, and blue-bit image. Subsequently, the
histograms of each red, blue, and blue-bit image were extended. After that, the hemorrhage candidates were detected.
The brown regions indicated hemorrhages and blood vessels and their candidates were detected using density analysis.
We removed the large candidates such as blood vessels. Finally, false positives were removed by using a 45-feature
analysis. To evaluate the new method for the detection of hemorrhages, we examined 125 fundus images, including 35
images with hemorrhages and 90 normal images. The sensitivity and specificity for the detection of abnormal cases was
were 80% and 88%, respectively. These results indicate that the new method may effectively improve the performance
of our computer-aided diagnosis system for hemorrhages.
Biometric technique has been implemented instead of conventional identification methods such as password in computer,
automatic teller machine (ATM), and entrance and exit management system. We propose a personal identification (PI)
system using color retinal fundus images which are unique to each individual. The proposed procedure for identification
is based on comparison of an input fundus image with reference fundus images in the database. In the first step,
registration between the input image and the reference image is performed. The step includes translational and rotational
movement. The PI is based on the measure of similarity between blood vessel images generated from the input and
reference images. The similarity measure is defined as the cross-correlation coefficient calculated from the pixel values.
When the similarity is greater than a predetermined threshold, the input image is identified. This means both the input
and the reference images are associated to the same person. Four hundred sixty-two fundus images including forty-one
same-person's image pairs were used for the estimation of the proposed technique. The false rejection rate and the false
acceptance rate were 9.9×10<sup>-5</sup>% and 4.3×10<sup>-5</sup>%, respectively. The results indicate that the proposed method has a higher
performance than other biometrics except for DNA. To be used for practical application in the public, the device which
can take retinal fundus images easily is needed. The proposed method is applied to not only the PI but also the system
which warns about misfiling of fundus images in medical facilities.
This paper describes a method for detecting hemorrhages and exudates in ocular fundus images. The detection of
hemorrhages and exudates is important in order to diagnose diabetic retinopathy. Diabetic retinopathy is one of the most
significant factors contributing to blindness, and early detection and treatment are important. In this study, hemorrhages
and exudates were automatically detected in fundus images without using fluorescein angiograms. Subsequently, the
blood vessel regions incorrectly detected as hemorrhages were eliminated by first examining the structure of the blood
vessels and then evaluating the length-to-width ratio. Finally, the false positives were eliminated by checking the
following features extracted from candidate images: the number of pixels, contrast, 13 features calculated from the co-occurrence
matrix, two features based on gray-level difference statistics, and two features calculated from the extrema
method. The sensitivity of detecting hemorrhages in the fundus images was 85% and that of detecting exudates was
77%. Our fully automated scheme could accurately detect hemorrhages and exudates.
Retinal nerve fiber layer defect (NFLD) is one of the most important findings for the diagnosis of glaucoma reported by
ophthalmologists. However, such changes could be overlooked, especially in mass screenings, because ophthalmologists
have limited time to search for a number of different changes for the diagnosis of various diseases such as diabetes,
hypertension and glaucoma. Therefore, the use of a computer-aided detection (CAD) system can improve the results of
diagnosis. In this work, a technique for the detection of NFLDs in retinal fundus images is proposed. In the
preprocessing step, blood vessels are "erased" from the original retinal fundus image by using morphological filtering.
The preprocessed image is then transformed into a rectangular array. NFLD regions are observed as vertical dark bands
in the transformed image. Gabor filtering is then applied to enhance the vertical dark bands. False positives (FPs) are
reduced by a rule-based method which uses the information of the location and the width of each candidate region. The
detected regions are back-transformed into the original configuration. In this preliminary study, 71% of NFLD regions
are detected with average number of FPs of 3.2 per image. In conclusion, we have developed a technique for the
detection of NFLDs in retinal fundus images. Promising results have been obtained in this initial study.
The analysis of the optic nerve head (ONH) in the retinal fundus is important for the early detection of glaucoma. In this
study, we investigate an automatic reconstruction method for producing the 3-D structure of the ONH from a stereo
retinal image pair; the depth value of the ONH measured by using this method was compared with the measurement
results determined from the Heidelberg Retina Tomograph (HRT). We propose a technique to obtain the depth value
from the stereo image pair, which mainly consists of four steps: (1) cutout of the ONH region from the retinal images,
(2) registration of the stereo pair, (3) disparity detection, and (4) depth calculation. In order to evaluate the accuracy of
this technique, the shape of the depression of an eyeball phantom that had a circular dent as generated from the stereo
image pair and used to model the ONH was compared with a physically measured quantity. The measurement results
obtained when the eyeball phantom was used were approximately consistent. The depth of the ONH obtained using the
stereo retinal images was in accordance with the results obtained using the HRT. These results indicate that the stereo
retinal images could be useful for assessing the depth of the ONH for the diagnosis of glaucoma.