A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering

Detection of optic disc in retinal images by means of a geometrical model of vessel structure

We present here a new method to identify the position of the optic disc (OD) in retinal fundus images. The method is based on the preliminary detection of the main retinal vessels. All retinal vessels originate from the OD and their path follows a similar directional pattern (parabolic course) in all images. To describe the general direction of retinal vessels at any given position in the image, a geometrical parametric model was proposed, where two of the model parameters are the coordinates of the OD center. Using as experimental data samples of vessel centerline points and corresponding vessel directions, provided by any vessel identification procedure, model parameters were identified by means of a simulated annealing optimization technique. These estimated values provide the coordinates of the center of OD. A Matlab prototype implementing this method was developed. An evaluation of the proposed procedure was performed using the set of 81 images from the STARE project, containing images from both normal and pathological subjects. The OD position was correctly identified in 79 out of 81 images (98%), even in rather difficult pathological situations.

A computer-aided diagnosis for locating abnormalities in bone scintigraphy by a fuzzy system with a three-step minimization approach

Bone scintigraphy is an effective method to diagnose bone diseases such as bone tumors. In the scintigraphic images, bone abnormalities are widely scattered on the whole body. Conventionally, radiologists visually check the whole-body images and find the distributed abnormalities based on their expertise. This manual process is time-consuming and it is not unusual to miss some abnormalities. In this paper, a computer-aided diagnosis (CAD) system is proposed to assist radiologists in the diagnosis of bone scintigraphy. The system will provide warning marks and abnormal scores on some locations of the images to direct radiologists' attention toward these locations. A fuzzy system called characteristic-point-based fuzzy inference system (CPFIS) is employed to implement the diagnosis system and three minimizations are used to systematically train the CPFIS. Asymmetry and brightness are chosen as the two inputs to the CPFIS according to radiologists' knowledge. The resulting CAD system is of a small-sized rule base such that the resulting fuzzy rules can be not only easily understood by radiologists, but also matched to and compared with their expert knowledge. The prototype CAD system was tested on 82 abnormal images and 27 normal images. We employed free-response receiver operating characteristics method with the mean number of false positives (FPs) and the sensitivity as performance indexes to evaluate the proposed system. The sensitivity is 91.5% (227 of 248) and the mean number of FPs is 37.3 per image. The high sensitivity and moderate numbers of FP marks per image shows that the proposed method can provide an effective second-reader information to radiologists in the diagnosis of bone scintigraphy.

A specialized plug-in software module for computer-aided quantitative measurement of medical images

This paper presents a specialized system for quantitative measurement of medical images. Using Visual C++, we developed a computer-aided software based on Image-Pro Plus (IPP), a software development platform. When transferred to the hard disk of a computer by an MVPCI-V3A frame grabber, medical images can be automatically processed by our own IPP plug-in for immunohistochemical analysis, cytomorphological measurement and blood vessel segmentation. In 34 clinical studies, the system has shown its high stability, reliability and ease of utility.

Ridge-based vessel segmentation in color images of the retina

A method is presented for automated segmentation of vessels in two-dimensional color images of the retina. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. The system is based on extraction of image ridges, which coincide approximately with vessel centerlines. The ridges are used to compose primitives in the form of line elements. With the line elements an image is partitioned into patches by assigning each image pixel to the closest line element. Every line element constitutes a local coordinate frame for its corresponding patch. For every pixel, feature vectors are computed that make use of properties of the patches and the line elements. The feature vectors are classified using a kappaNN-classifier and sequential forward feature selection. The algorithm was tested on a database consisting of 40 manually labeled images. The method achieves an area under the receiver operating characteristic curve of 0.952. The method is compared with two recently published rule-based methods of Hoover et al. and Jiang et al. The results show that our method is significantly better than the two rule-based methods (p < 0.01). The accuracy of our method is 0.944 versus 0.947 for a second observer.

Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels

We describe an automated method to locate the optic nerve in images of the ocular fundus. Our method uses a novel algorithm we call fuzzy convergence to determine the origination of the blood vessel network. We evaluate our method using 31 images of healthy retinas and 50 images of diseased retinas, containing such diverse symptoms as tortuous vessels, choroidal neovascularization, and hemorrhages that completely obscure the actual nerve. On this difficult data set, our method achieved 89% correct detection. We also compare our method against three simpler methods, demonstrating the performance improvement. All our images and data are freely available for other researchers to use in evaluating related methods.

Retinal vascular tree morphology: a semi-automatic quantification

A semi-automatic method to measure and quantify geometrical and topological properties of continuous vascular trees in clinical fundus images is described. Measurements are made from binary images obtained with a previously described segmentation process. The skeletons of the segmented trees are produced by thinning,ff branch and crossing points are identified and segments of the trees are labeled and stored as a chain code. The operator selects a tree to be measured and decides if it is an arterial or venous tree. An automatic process then measures the lengths, areas and angles of the individual segments of the tree. Geometrical data and the connectivity information between branches from continuous retinal vessel trees are tabulated. A number of geometrical properties and topological indexes are derived. Vessel diameters and branching angles are validated against manual measurements and several derived geometrical and topological properties are extracted from red-free fundus images of ten normotensive and ten age- and sex-matched hypertensive subjects and compared with previously reported results.

Progress towards automated diabetic ocular screening: a review of image analysis and intelligent systems for diabetic retinopathy

Patients with diabetes require annual screening for effective timing of sight-saving treatment. However, the lack of screening and the shortage of ophthalmologists limit the ocular health care available. This is stimulating research into automated analysis of the reflectance images of the ocular fundus. Publications applicable to the automated screening of diabetic retinopathy are summarised. The review has been structured to mimic some of the processes that an ophthalmologist performs when examining the retina. Thus image processing tasks, such as vessel and lesion location, are reviewed before any intelligent or automated systems. Most research has been undertaken in identification of the retinal vasculature and analysis of early pathological changes. Progress has been made in the identification of the retinal vasculature and the more common pathological features, such as small aneurysms and exudates. Ancillary research into image preprocessing has also been identified. In summary, the advent of digital data sets has made image analysis more accessible, although questions regarding the assessment of individual algorithms and whole systems are only just being addressed.

Estimation of expiratory time constants via fuzzy clustering

OBJECTIVE

In mechanically ventilated patients the expiratory time constant provides information about respiratory mechanics. In the present study a new method, fuzzy clustering, is proposed to determine expiratory time constants. Fuzzy clustering differs from other methods since it neither interferes with expiration nor presumes any functional relationship between the variables analysed. Furthermore, time constant behaviour during expiration can be assessed, instead of an average time constant. The time constants obtained with fuzzy clustering are compared to time constants conventionally calculated from the same expirations.

METHODS

20 mechanically ventilated patients, including 10 patients with COPD, were studied. The data of flow, volume and pressure were sampled. From these data, four local linear models were detected by fuzzy clustering. The time constants (tau) of the local linear models (clusters) were calculated by a least-squares technique. Time constant behaviour was analysed. Time constants obtained with fuzzy clustering were compared to time constants calculated from flow-volume curves using a conventional method.

RESULTS

Fuzzy clustering revealed two patterns of expiratory time constant behaviour. In the patients with COPD an initial low time constant was found (mean tau1: 0.33 s, SD 0.21) followed by higher time constants; mean tau2: 2.00 s (SD 0.91s), mean tau3: 3.45 s (SD 1.44) and mean tau4: 5.47 s (SD 2.93). In the other patients only minor changes in time constants were found; mean tau1: 0.74 s (SD 0.30), mean tau2: 0.90 s (SD 0.23), mean tau3: 1.04 s (SD 0.42) and mean tau4: 1.74 s (SD 0.78). Both the pattern of expiratory time constants, as well as the time constants calculated from the separate clusters, were significantly different between the patients with and without COPD. Time constants obtained with fuzzy clustering for cluster 2, 3 and 4 correlated well with time constants obtained from the flow-volume curves.

CONCLUSIONS

In mechanically ventilated patients, expiratory time constant behaviour can be accurately assessed by fuzzy clustering. A good correlation was found between time constants obtained with fuzzy clustering and time constants obtained by conventional analysis. On the basis of the time constants obtained with fuzzy clustering, a clear distinction was made between patients with and without

Retinal microvascular abnormalities and their relationship with hypertension, cardiovascular disease, and mortality

Retinal microvascular abnormalities, such as generalized and focal arteriolar narrowing, arteriovenous nicking and retinopathy, reflect cumulative vascular damage from hypertension, aging, and other processes. Epidemiological studies indicate that these abnormalities can be observed in 2-15% of the nondiabetic general population and are strongly and consistently associated with elevated blood pressure. Generalized arteriolar narrowing and arteriovenous nicking also appear to be irreversible long-term markers of hypertension, related not only to current but past blood pressure levels as well. There are data supporting an association between retinal microvascular abnormalities and stroke, but there is no convincing evidence of an independent or direct association with atherosclerosis, ischemic heart disease, or cardiovascular mortality. New computer-related imaging methods are currently being developed to detect the presence and severity of retinal arteriolar narrowing and other microvascular characteristics. When reliably quantified, retinal microvascular abnormalities may be useful as risk indicators for cerebrovascular diseases.

Estimation of respiratory parameters via fuzzy clustering

The results of monitoring respiratory parameters estimated from flow-pressure-volume measurements can be used to assess patients' pulmonary condition, to detect poor patient-ventilator interaction and consequently to optimize the ventilator settings. A new method is proposed to obtain detailed information about respiratory parameters without interfering with the expiration. By means of fuzzy clustering, the available data set is partitioned into fuzzy subsets that can be well approximated by linear regression models locally. Parameters of these models are then estimated by least-squares techniques. By analyzing the dependence of these local parameters on the location of the model in the flow-volume-pressure space, information on patients' pulmonary condition can be gained. The effectiveness of the proposed approaches is demonstrated by analyzing the dependence of the expiratory time constant on the volume in patients with chronic obstructive pulmonary disease (COPD) and patients without COPD.

Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response

We describe an automated method to locate and outline blood vessels in images of the ocular fundus. Such a tool should prove useful to eye care specialists for purposes of patient screening, treatment evaluation, and clinical study. Our method differs from previously known methods in that it uses local and global vessel features cooperatively to segment the vessel network. We evaluate our method using hand-labeled ground truth segmentations of 20 images. A plot of the operating characteristic shows that our method reduces false positives by as much as 15 times over basic thresholding of a matched filter response (MFR), at up to a 75% true positive rate. For a baseline, we also compared the ground truth against a second hand-labeling, yielding a 90% true positive and a 4% false positive detection rate, on average. These numbers suggest there is still room for a 15% true positive rate improvement, with the same false positive rate, over our method. We are making all our images and hand labelings publicly available for interested researchers to use in evaluating related methods.