Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or non-vessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and continuous two-dimensional Morlet wavelet transform responses taken at multiple scales. The Morlet wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces and compare its performance with the linear minimum squared error classifier. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE and STARE databases of manually labeled non-mydriatic images. On the DRIVE database, it achieves an area under the receiver operating characteristic (ROC) curve of 0.9598, being slightly superior than that presented by the method of Staal et al.Comment: 9 pages, 7 figures and 1 table. Accepted for publication in IEEE Trans Med Imag; added copyright notic

Trainable COSFIRE filters for vessel delineation with application to retinal images

Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to such diagnosis. We introduce a novel method for the automatic segmentation of vessel trees in retinal fundus images. We propose a filter that selectively responds to vessels and that we call B-COSFIRE with B standing for bar which is an abstraction for a vessel. It is based on the existing COSFIRE (Combination Of Shifted Filter Responses) approach. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians filters, whose supports are aligned in a collinear manner. It achieves rotation invariance efficiently by simple shifting operations. The proposed filter is versatile as its selectivity is determined from any given vessel-like prototype pattern in an automatic configuration process. We configure two B-COSFIRE filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel segmentation by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding. The results that we achieve on three publicly available data sets (DRIVE: Se = 0.7655, Sp = 0.9704; STARE: Se = 0.7716, Sp = 0.9701; CHASE_DB1: Se = 0.7585, Sp = 0.9587) are higher than many of the state-of-the-art methods. The proposed segmentation approach is also very efficient with a time complexity that is significantly lower than existing methods.peer-reviewe

Automatic Segmentation of Retinal Vasculature

Segmentation of retinal vessels from retinal fundus images is the key step in the automatic retinal image analysis. In this paper, we propose a new unsupervised automatic method to segment the retinal vessels from retinal fundus images. Contrast enhancement and illumination correction are carried out through a series of image processing steps followed by adaptive histogram equalization and anisotropic diffusion filtering. This image is then converted to a gray scale using weighted scaling. The vessel edges are enhanced by boosting the detail curvelet coefficients. Optic disk pixels are removed before applying fuzzy C-mean classification to avoid the misclassification. Morphological operations and connected component analysis are applied to obtain the segmented retinal vessels. The performance of the proposed method is evaluated using DRIVE database to be able to compare with other state-of-art supervised and unsupervised methods. The overall segmentation accuracy of the proposed method is 95.18% which outperforms the other algorithms.Comment: Published at IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 201

A Rule Based Segmentation Approaches to Extract Retinal Blood Vessels in Fundus Image

The physiological structures of the retinal blood vessel are one of the key features that visible in the retinal images and contain the information associate with the anatomical abnormalities. It is accepted all over the world to judge the cardiovascular and retinal disease. To avoid the risk of visual impairment, appropriate vessel segmentation is mandatory. Here has proposed a segmentation algorithm that efficiently extracts the blood vessels from the retinal fundus image. The proposed segmentation algorithm is performed Lab and Principle Component (PC) based gray level conversion, Contrast Limited Adaptive Histogram Equalization (CLAHE), morphological operations, Local Property-Based Pixel Correction (LPBPC). For appropriate detection proposed vessels correction algorithm LPBPC that check the feature of the vessels and remove the wrong vessel detection. To measure the appropriateness of the proposed algorithm, the experimental results are compared with the corresponding ground truth images. The experimental results have shown that the proposed blood vessel algorithm is more accurate than the existing algorithms

Segmentasi Pembuluh Darah Retina Menggunakan Local Adaptive Thresholding

Pembuluh darah retina yang memiliki pola dan ciri-ciri tertentu dapat kita gunakan untuk mendiagnosa suatu penyakit. Dalam penelitian ini akan dibahas mengenai hasil segmentasi citra pembuluh darah retina menggunakan metode Local Adaptive Thresholding. Local Adaptive Thresholding merupakan suatu metode dimana dalam pencarian nilai ambang batas, gambar dipecah menjadi beberapa bagian gambar yang lebih kecil kemudian tiap-tiap bagian gambar tersebut akan dicari nilai ambang batasnya. Sebelum masuk ke proses segmentasi dilakukan proses preprocessing terhadap citra retina untuk memperbaiki kualitas dari citra tersebut. Proses preprocessing tersebut yaitu ekstrasi kanal hijau dan CLAHE. Dari hasil segmentasi yang dilakukan terhadap 5 buah gambar yang diambil secara acak dari dataset STARE didapatkan nilai PNSR rata-rata diatas 30

Perbandingan Antara Metode Otsu Thresholding dan Multilevel Thresholding untuk Segmentasi Pembuluh Darah Retina

Pembuluh darah retina dapat kita gunakan untuk mendiagnosa suatu penyakit dikarenakan memiliki pola dan ciri-ciri tertentu. Dalam penelitian ini akan dibahas mengenai hasil segmentasi citra pembuluh darah retina dengan menggunakan dua metode segmentasi citra yakni metode Otsu Thresholding dan Multilevel Thresholding merupakan metode segmentasi yang menggunakan pemilihan ambang batas secara otomatis dari tingkat keabu-abuan. Hasil penelitian menunjukkan bahwa metode Multilevel Thresholdng menghasilkan kinerja tinggi seperti yang terlihat dari nilai Peak Signal-to-Ratio (PSNR) dan Root Mean Square Error (RMSE). Penelitian ini menggunakan dataset STARE yang tersedia untuk riset yang diambil sebagai evaluasi kinerja metode yang diteliti. Dari dua metode segmentasi yang diusulkan, metode Otsu Thresholding lebih baik dengan memiliki nilai RMSE yang lebih kecil dan nilai PSNR yang lebih besar dibandingkan dengan metode Multilevel Thresholding dengan nilai rata-rata RMSE sebesar 8.5832, dan nilai rata-rata PSNR sebesar 49.6459