Sudeshna Sil Kar

Retinal blood vessel extraction using tunable bandpass filter and fuzzy conditional entropy

BACKGROUND AND OBJECTIVES Extraction of blood vessels on retinal images plays a significant role for screening of different opthalmologic diseases. However, accurate extraction of the entire and individual type of vessel silhouette from the noisy images with poorly illuminated background is a complicated task. To this aim, an integrated system design platform is suggested in this work for vessel extraction using a sequential bandpass filter followed by fuzzy conditional entropy maximization on matched filter response. METHODS At first noise is eliminated from the image under consideration through curvelet based denoising. To include the fine details and the relatively less thick vessel structures, the image is passed through a bank of sequential bandpass filter structure optimized for contrast enhancement. Fuzzy conditional entropy on matched filter response is then maximized to find the set of multiple optimal thresholds to extract the different types of vessel silhouettes from the background. Differential Evolution algorithm is used to determine the optimal gain in bandpass filter and the combination of the fuzzy parameters. Using the multiple thresholds, retinal image is classified as the thick, the medium and the thin vessels including neovascularization. RESULTS Performance evaluated on different publicly available retinal image databases shows that the proposed method is very efficient in identifying the diverse types of vessels. Proposed method is also efficient in extracting the abnormal and the thin blood vessels in pathological retinal images. The average values of true positive rate, false positive rate and accuracy offered by the method is 76.32%, 1.99% and 96.28%, respectively for the DRIVE database and 72.82%, 2.6% and 96.16%, respectively for the STARE database. Simulation results demonstrate that the proposed method outperforms the existing methods in detecting the various types of vessels and the neovascularization structures. CONCLUSIONS The combination of curvelet transform and tunable bandpass filter is found to be very much effective in edge enhancement whereas fuzzy conditional entropy efficiently distinguishes vessels of different widths.

Extraction of Retinal Blood Vessel Using Curvelet Transform and Fuzzy C-Means

This paper addresses the automatic blood vessel detection problem in retinal images using matched filtering in an integrated system design platform that involves curve let transform and fuzzy c-means. Although noise is kept constant in medical CCD cameras, due to a number of factors, the contrast between the background and the blood vessels in retinal images and consequently the visual quality of the images looks very poor. Some form of pre-processing operation is therefore essential for the accurate extraction of these blood vessels. Since curve let transform can represent lines, edges and curvatures very well as compared to other multi-resolution techniques, this paper uses curve let transform to enhance the retinal vasculature. Matched filtering is then used to intensify the blood vessels which is further employed by fuzzy c-means algorithm to extract the vessel silhouette from the background. Performance is evaluated on publicly available DRIVE database and is compared with the existing blood vessel extraction methodology that uses curve let transform. Simulation results demonstrate that the proposed method is very much efficient in detecting long and thick as well as short and thin vessels, wherein the existing methods fail to extract tiny and thin vessels.

Retinal blood vessel segmentation using matched filter and Laplacian of Gaussian

Automated blood vessel segmentation of retinal images offers huge potential benefits for medical diagnosis of different ocular diseases. In this paper, 2D Matched Filters (MF) are applied to fundus retinal images to detect vessels which are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE) method. Due to the Gaussian nature of blood vessel profile, the MF with Gaussian kernel often misclassifies non-vascular structures (e.g., step, ramp or other transients) as vessels. To avoid such false detection, this paper introduces Laplacian of Gaussian (LoG) filters in the vessel segmentation process. The inherent zero-crossing property of LoG filter is used in the algorithm, along with the MF, in order to extract vessels reliably from the retinal images. The proposed method is validated against three publicly available databases, STARE, DRIVE and HRF. Simulation results show that the proposed method is able to segment vessels accurately from the three database images with an average accuracy that is competitive to the existing methodologies.

Automatic Detection of Retinal Lesions for Screening of Diabetic Retinopathy

Objective: Diabetic retinopathy (DR) is characterized by the progressive deterioration of retina with the appearance of different types of lesions that include microaneurysms, hemorrhages, exudates, etc. Detection of these lesions plays a significant role for early diagnosis of DR. Methods: To this aim, this paper proposes a novel and automated lesion detection scheme, which consists of the four main steps: vessel extraction and optic disc removal, preprocessing, candidate lesion detection, and postprocessing. The optic disc and the blood vessels are suppressed first to facilitate further processing. Curvelet-based edge enhancement is done to separate out the dark lesions from the poorly illuminated retinal background, while the contrast between the bright lesions and the background is enhanced through an optimally designed wideband bandpass filter. The mutual information of the maximum matched filter response and the maximum Laplacian of Gaussian response are then jointly maximized. Differential evolution algorithm is used to determine the optimal values for the parameters of the fuzzy functions that determine the thresholds of segmenting the candidate regions. Morphology-based postprocessing is finally applied to exclude the falsely detected candidate pixels. Results and Conclusions: Extensive simulations on different publicly available databases highlight an improved performance over the existing methods with an average accuracy of

Gradation of diabetic retinopathy on reconstructed image using compressed sensing

97.71\%

Detection of neovascularization in retinal images using mutual information maximization

and robustness in detecting the various types of DR lesions irrespective of their intrinsic properties.

Differential evolution based optimal clustering for retinal blood vessel extraction

This study explores neovascularisation and lesion detection in an integrated framework for gradation in diabetic retinopathy (DR). Imaging is assumed to be done from sub-sample measurements following compressed sensing. Blind estimation of the scale of the matched filter (MF) followed by fuzzy entropy maximisation is done for extraction and classification of the thick and the thin vessels. Mutual information (MI) between vessel density and tortuosity of the thin vessel class is maximised in two dimensions (2D) for neovascularisation detection. For lesion detection, MI between the maximum MF response and the maximum Laplacian of Gaussian filter response is jointly maximised in 2D. The outcomes are then combined in a common platform for gradation in DR. Simulation results demonstrate that 95% images of each of DRIVE, STARE and DIARETDB1 databases and 94% images of MESSIDOR database are correctly graded by the proposed method when 80% measurement space is considered.

Neovascularization Detection on Retinal Images

Abstract Proliferative diabetic retinopathy (PDR) is characterized by the proliferation of new abnormal blood vessels (neovascularization) that cause their detachment from retina. An automated computer aided diagnosis (CAD) system is developed for neovascularization (new abnormal blood vessel) detection on retinal images. Curvelet transform is used to intensify the fine details of the vascular network followed by maximization of mutual information (MI) on the maximum matched filter response for optimal thresholding to partition the vessels into the thick and the thin categories. Vessel density and tortuosity, being two unique and distinct features for the abnormal vessels, are calculated from the thin vessel class followed by MI maximization and post-processing for neovascularization detection. Simulation results demonstrate that an average accuracy gain of 97.49% is achieved by the proposed method in abnormal vessel detection over the existing methods.

Retinal Blood Vessel Extraction and Optic Disc Removal Using Curvelet Transform and Morphological Operation

Design of a computer-aided automatic system is very important for identification of different ocular diseases. A vital concern within this framework is the accurate retinal blood vessel extraction. This paper extracts vessels using curvelet transform, morphological operation, matched filtering and Differential Evolution based optimal clustering. Curvelet transform is implemented to enhance vessel edges. To remove the optic disc, the edge enhanced image is opened by a disk shaped structuring element which is then subtracted from the inverted histogram equalized image. Matched filtering intensifies the blood vessels response. The classification of the maximum matched filter responses into vessel and non-vessel classes is considered as a multi-objective clustering problem that minimizes the intracluster distances and maximizes the inter-cluster distance which is solved by Differential Evolution. Superiority of the proposed method is demonstrated by comparing it with the existing methods.

Retinal blood vessel extraction using curvelet transform and conditional fuzzy entropy

Proliferative Diabetic Retinopathy (PDR) is characterized by the growth of new abnormal, thin blood vessels called neovascularzation that spread along the retinal surface. An automated computer aided diagnosis system needs to identify neovasculars for PDR screening. Retinal images are often noisy and poorly illuminated. The thin vessels mostly appear to be disconnected and are inseparable from the background. This paper proposes a new method for neovascularization detection on retinal images. Blood vessels are extracted as thick, medium and thin types using multilevel thresholding on matched filter response. The total mutual information between the vessel density and the tortuosity of the thin vessel class is maximized to obtain the optimal thresholds to classify the normal and the abnormal vessels. Simulation results demonstrate that the proposed method outperforms the existing ones for neovascularization detection with an average accuracy of \(97.54\%\).