Early detection of glaucoma is important to slow down progression of the disease and to prevent total vision loss. When the retinal nerve is damaged, the thickness of the nerve fiber layer decreases. It is difficult, however, to detect subtle change in early disease stages on retinal fundus photographs. Although an optical coherence tomography (OCT) is generally more sensitive and can evaluate the thicknesses of retinal layers, it is performed as a diagnostic exam rather than screening exam. Retinal fundus photographs are frequently performed for diagnosis and follow-ups at ophthalmology visits and for general health checkups. It will be useful if suspicious regions can be detected on retinal photographs. The purpose of this study is to estimate the regions of defected nerves on retinal photographs using the deep learning model trained by OCT data. The network is based on the fully convolutional network. The region including an optic disc is extracted from the retinal photographs and is used as the input data. The OCT image of the same patient is registrated to the retinal image based on the blood vessel networks, and the deviation map specifying the regions with decreased nerve layer thickness is used as teacher data. The proposed method achieved 76% accuracy in assessing the defected and non-defected regions. It can be useful as a screening tool and for visual assistance in glaucoma diagnosis.
Early detection of hypertension is important because hypertension leads to stroke and cardiovascular diseases. Hypertensive changes in the retina are diagnosed by measuring the arteriovenous ratio near the optic disc. Therefore, classification of arteries and veins is necessary for ratio measurement, and previous studies classified them by using pixel-based features, such as pixel values, texture features, and shape features etc. For simplification of the classification process, a convolutional neural network (CNN) was applied in this study. For evaluation of the classification process, CNN was tested using centerlines extracted manually in this study. As a result of a fourfold cross-validation with 40 retinal images, the mean classification ratio of the arteries and veins was 98%. Furthermore, CNN was tested using the centerlines of blood vessels automatically extracted using the CNN-based method for testing the fully automatic method. CNN classified 90% of blood vessels into arteries and veins in the arteriovenous ratio measurement zone. CNN had 30 trained and 10 tested retinal images. This result may work as an important processing for abnormality detection.
Several studies for layout design optimization depend on evaluation indices with necessary passage, spaciousness, etc. It is difficult to obtain a friendly layout by using conventional methods. The layout design-aided model in this study is a residence space, and there are eight types of furniture. All furniture is first allocated to each room by using a genetic algorithm. All allocated furniture’s initial arrangements in each room are then determined by using Q-learning. A user checks the initial layout through virtual reality and evaluates it subjectively. The layout for a specific user is flexibly fixed by applying Q-learning, and a user subjective reward is added. As a result of observer experiments, more than half of the furniture can be arranged in an ideal position for a user, and a satisfactory layout is successfully generated.
Early detection of glaucoma is important to slow down progression of the disease and to prevent total vision loss. We have been studying an automated scheme for detection of a retinal nerve fiber layer defect (NFLD), which is one of the earliest signs of glaucoma on retinal fundus images. In our previous study, we proposed a multi-step detection scheme which consists of Gabor filtering, clustering and adaptive thresholding. The problems of the previous method were that the number of false positives (FPs) was still large and that the method included too many rules. In attempt to solve these problems, we investigated the end-to-end learning system without pre-specified features. A deep convolutional neural network (DCNN) with deconvolutional layers was trained to detect NFLD regions. In this preliminary investigation, we investigated effective ways of preparing the input images and compared the detection results. The optimal result was then compared with the result obtained by the previous method. DCNN training was carried out using original images of abnormal cases, original images of both normal and abnormal cases, ellipse-based polar transformed images, and transformed half images. The result showed that use of both normal and abnormal cases increased the sensitivity as well as the number of FPs. Although NFLDs are visualized with the highest contrast in green plane, the use of color images provided higher sensitivity than the use of green image only. The free response receiver operating characteristic curve using the transformed color images, which was the best among seven different sets studied, was comparable to that of the previous method. Use of DCNN has a potential to improve the generalizability of automated detection method of NFLDs and may be useful in assisting glaucoma diagnosis on retinal fundus images.
Early detection of glaucoma is important to slow down or cease progression of the disease and for preventing total blindness. We have previously proposed an automated scheme for detection of retinal nerve fiber layer defect (NFLD), which is one of the early signs of glaucoma observed on retinal fundus images. In this study, a new multi-step detection scheme was included to improve detection of subtle and narrow NFLDs. In addition, new features were added to distinguish between NFLDs and blood vessels, which are frequent sites of false positives (FPs). The result was evaluated with a new test dataset consisted of 261 cases, including 130 cases with NFLDs. Using the proposed method, the initial detection rate was improved from 82% to 98%. At the sensitivity of 80%, the number of FPs per image was reduced from 4.25 to 1.36. The result indicates the potential usefulness of the proposed method for early detection of glaucoma.
An automated blood vessel extraction using high-order local autocorrelation (HLAC) on retinal images is presented. Although many blood vessel extraction methods based on contrast have been proposed, a technique based on the relation of neighbor pixels has not been published. HLAC features are shift-invariant; therefore, we applied HLAC features to retinal images. However, HLAC features are weak to turned image, thus a method was improved by the addition of HLAC features to a polar transformed image. The blood vessels were classified using an artificial neural network (ANN) with HLAC features using 105 mask patterns as input. To improve performance, the second ANN (ANN2) was constructed by using the green component of the color retinal image and the four output values of ANN, Gabor filter, double-ring filter and black-top-hat transformation. The retinal images used in this study were obtained from the "Digital Retinal Images for Vessel Extraction" (DRIVE) database. The ANN using HLAC output apparent white values in the blood vessel regions and could also extract blood vessels with low contrast. The outputs were evaluated using the area under the curve (AUC) based on receiver operating characteristics (ROC) analysis. The AUC of ANN2 was 0.960 as a result of our study. The result can be used for the quantitative analysis of the blood vessels.
Glaucoma is one of the leading causes of blindness in Japan and the US. One of the indices for diagnosis of glaucoma is the cup-to-disc ratio (CDR). We have been developing a computerized method for measuring CDR on stereo fundus photographs. Although our previous study indicated that the method may be useful, cup determination was not always successful, especially for the normal eyes. In this study, we investigated a new method to quantify the likelihood of glaucomatous disc based on the similarity scores to the glaucoma and non-glaucoma models. Eighty-seven images, including 40 glaucomatous eyes, were used in this study. Only one eye from each patient was used. Using a stereo fundus camera, two images were captured from different angles, and the depth image was created by finding the local corresponding points. One of the characteristics of a glaucomatous disc can be not only that the cup is enlarged but it has an acute slope. On the other hand, a non-glaucomatous cup generally has a gentle slope. Therefore, our models were constructed by averaging the depth gradient images. In order to account for disc size, disc outline was automatically detected, and all images were registered by warping the disc outline to a circle with a predetermined diameter using thin plate splines. Similarity scores were determined by multiplying a test case with both models. At the sensitivity of 90.0%, the specificity was improved from 83.0% using the CDR to 97.9% by the model-based method. The proposed method may be useful for differentiation of glaucomatous eyes.
Early diagnosis of glaucoma, which is the second leading cause of blindness in the world, can halt or slow the progression of the disease. We propose an automated method for analyzing the optic disc and measuring the cup-to-disc ratio (CDR) on stereo retinal fundus images to improve ophthalmologists' diagnostic efficiency and potentially reduce the variation on the CDR measurement. The method was developed using 80 retinal fundus image pairs, including 25 glaucomatous, and 55 nonglaucomatous eyes, obtained at our institution. A disc region was segmented using the active contour method with the brightness and edge information. The segmentation of a cup region was performed using a depth map of the optic disc, which was reconstructed on the basis of the stereo disparity. The CDRs were measured and compared with those determined using the manual segmentation results by an expert ophthalmologist. The method was applied to a new database which consisted of 98 stereo image pairs including 60 and 30 pairs with and without signs of glaucoma, respectively. Using the CDRs, an area under the receiver operating characteristic curve of 0.90 was obtained for classification of the glaucomatous and nonglaucomatous eyes. The result indicates potential usefulness of the automated determination of CDRs for the diagnosis of glaucoma.
Arteriolosclerosis is one cause of acquired blindness. Retinal fundus image examination is useful for early detection of
arteriolosclerosis. In order to diagnose the presence of arteriolosclerosis, the physicians find the silver-wire arteries, the
copper-wire arteries and arteriovenous crossing phenomenon on retinal fundus images. The focus of this study was to
develop the automated detection method of the arteriovenous crossing phenomenon on the retinal images. The blood
vessel regions were detected by using a double ring filter, and the crossing sections of artery and vein were detected by
using a ring filter. The center of that ring was an interest point, and that point was determined as a crossing section when
there were over four blood vessel segments on that ring. And two blood vessels gone through on the ring were classified
into artery and vein by using the pixel values on red and blue component image. Finally, V2-to-V1 ratio was measured for
recognition of abnormalities. V1 was the venous diameter far from the blood vessel crossing section, and V2 was the
venous diameter near from the blood vessel crossing section. The crossing section with V2-to-V1 ratio over 0.8 was
experimentally determined as abnormality. Twenty four images, including 27 abnormalities and 54 normal crossing
sections, were used for preliminary evaluation of the proposed method. The proposed method was detected 73% of
crossing sections when the 2.8 sections per image were mis-detected. And, 59% of abnormalities were detected by
measurement of V1-to-V2 ratio when the 1.7 sections per image were mis-detected.
Abnormalities of retinal vasculatures can indicate health conditions in the body, such as the high blood pressure and
diabetes. Providing automatically determined width ratio of arteries and veins (A/V ratio) on retinal fundus images may
help physicians in the diagnosis of hypertensive retinopathy, which may cause blindness. The purpose of this study was
to detect major retinal vessels and classify them into arteries and veins for the determination of A/V ratio. Images used in
this study were obtained from DRIVE database, which consists of 20 cases each for training and testing vessel detection
algorithms. Starting with the reference standard of vasculature segmentation provided in the database, major arteries and
veins each in the upper and lower temporal regions were manually selected for establishing the gold standard. We
applied the black top-hat transformation and double-ring filter to detect retinal blood vessels. From the extracted vessels,
large vessels extending from the optic disc to temporal regions were selected as target vessels for calculation of A/V
ratio. Image features were extracted from the vessel segments from quarter-disc to one disc diameter from the edge of
optic discs. The target segments in the training cases were classified into arteries and veins by using the linear
discriminant analysis, and the selected parameters were applied to those in the test cases. Out of 40 pairs, 30 pairs (75%)
of arteries and veins in the 20 test cases were correctly classified. The result can be used for the automated calculation of
A/V ratio.
Glaucoma is a leading cause of permanent blindness. Retinal fundus image examination is useful for early detection of
glaucoma. In order to evaluate the presence of glaucoma, the ophthalmologists determine the cup and disc areas and they
diagnose glaucoma using a vertical cup-to-disc ratio. However, determination of the cup area is very difficult, thus we
propose a method to measure the cup-to-disc ratio using a vertical profile on the optic disc. First, the blood vessels were
erased from the image and then the edge of optic disc was then detected by use of a canny edge detection filter. Twenty
profiles were then obtained around the center of the optic disc in the vertical direction on blue channel of the color image,
and the profile was smoothed by averaging these profiles. After that, the edge of the cup area on the vertical profile was
determined by thresholding technique. Lastly, the vertical cup-to-disc ratio was calculated. Using seventy nine images,
including twenty five glaucoma images, the sensitivity of 80% and a specificity of 85% were achieved with this method.
These results indicated that this method can be useful for the analysis of the optic disc in glaucoma examinations.
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.
The presence of microaneurysms in the eye is one of the early signs of diabetic retinopathy, which is one of the leading
causes of vision loss. We have been investigating a computerized method for the detection of microaneurysms on retinal
fundus images, which were obtained from the Retinopathy Online Challenge (ROC) database. The ROC provides 50
training cases, in which "gold standard" locations of microaneurysms are provided, and 50 test cases without the gold
standard locations. In this study, the computerized scheme was developed by using the training cases. Although the
results for the test cases are also included, this paper mainly discusses the results for the training cases because the
"gold
standard" for the test cases is not known. After image preprocessing, candidate regions for microaneurysms were
detected using a double-ring filter. Any potential false positives located in the regions corresponding to blood vessels
were removed by automatic extraction of blood vessels from the images. Twelve image features were determined, and
the candidate lesions were classified into microaneurysms or false positives using the rule-based method and an artificial
neural network. The true positive fraction of the proposed method was 0.45 at 27 false positives per image. Forty-two
percent of microaneurysms in the 50 training cases were considered invisible by the consensus of two co-investigators.
When the method was evaluated for visible microaneurysms, the sensitivity for detecting microaneurysms was 65% at
27 false positives per image. Our computerized detection scheme could be improved for helping ophthalmologists in the
early diagnosis of diabetic retinopathy.
A large cup-to-disc (C/D) ratio, which is the ratio of the diameter of the depression (cup) to that of the optical nerve head
(ONH, disc), can be one of the important signs for diagnosis of glaucoma. Eighty eyes, including 25 eyes with the signs
of glaucoma, were imaged by a stereo retinal fundus camera. An ophthalmologist provided the outlines of cup and disc
on a regular monitor and on the stereo display. The depth image of the ONH was created by determining the
corresponding pixels in a pair of images based on the correlation coefficient in localized regions. The areas of the disc
and cup were determined by use of the red component in one of the color images and by use of the depth image,
respectively. The C/D ratio was determined based on the largest vertical lengths in the cup and disc areas, which was
then compared with that by the ophthalmologist. The disc areas determined by the computerized method agreed
relatively well with those determined by the ophthalmologist, whereas the agreement for the cup areas was somewhat
lower. When C/D ratios were employed for distinction between the glaucomatous and non-glaucomatous eyes, the area
under the receiver operating characteristic curve (AUC) was 0.83. The computerized analysis of ONH can be useful for
diagnosis of glaucoma.
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 9.9×10-5% and 4.3×10-5%, 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.
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.
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.
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.
We have developed a computer-aided diagnosis system to detect the abnormalities on retinal fundus images. In Japan, ophthalmologists usually detect hypertensive changes by identifying narrowing arteriolae with a focus on an irregularity. The purpose of this study is to develop an automated method for detecting narrowing arteriolae with a focus on an irregularity on retinal images. The blood vessel candidates were detected by the density analysis method. In blood vessel tracking, a local detection function was used to go along the centerline of the blood vessel. A direction comparison function using three vectors was designed to provide an optimal estimation of the next possible location of a blood vessel. After the connectivity of vessel segments was adjusted based on the recognized intersections, the true tree-like structure of the retinal blood vessels was established. The abnormal blood vessels were finally detected by measuring their diameters. The comparison between the results obtained using our system and the diagnostic results of physicians showed that our proposed method automatically detected an irregularity in diameter in 75% of all 24 narrowing arteries with a focus on an irregularity on 70 retinal fundus images. Approximately 2.88 normal vessel segments per image were determined to be abnormal, a number which must be reduced at the next stage. The automated detection of narrowing arteriolae with a focus on an irregularity could help ophthalmologists in diagnosing ocular diseases.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.