RETINAL BLOOD VESSELS SEGMENTATION BY CENTERLINES DETECTION AND MORPHOLOGICAL PROCESSING USING MULTI SCALE RECONSTRUCTION AND BIT PLANES

Abstract

Retrieval of retinal vascular network is used for diagnosis, treatment, screening, evaluation and clinical study of many diseases which induce changes in retinal vascular network. Blood vessels are the predominant and most stable structures appearing in the retina; therefore reliable vessel extraction is a prerequisite for subsequent retinal image analysis and processing. This work presents a new approach to extract the vascular tree from monochromatic retinal images by combining the detection of vessel centerlines with morphological processing. Vascular skeleton is acquired by detecting vessel centerlines and segmented vascular image is obtained by a sequence of morphological operations. The vessel centerlines, considered as local intensity maxima along vessel cross profiles are extracted by the application of directional differential operators and then evaluation of combination of derivative signs and average derivative values. Two separate morphological image processing methodologies are exploited for vessel segmentation. In multi scale morphological reconstruction, the vessels are enhanced by applying a modified top hat operator with variable size circular structuring elements aiming at enhancement of vessels with different widths. The binary maps of the vessels are obtained at four scales by using morphological reconstruction with double threshold operator. A final image with the segmented vessels is obtained by iterative seeded region growing process of the centerline image with the set of four binary maps. In morphological bit plane slicing, a multidirectional top hat operator with rotating structuring elements is adapted with the Gaussian-like profile of vessel and later bit plane slicing is used to extract visual information of vascular network. A region growing method is applied to integrate the centerline and the images resulting from bit plane slicing of vessel direction dependent morphological filters. These methods are evaluated using the images of two publicly available databases, the DRIVE database and the STARE database. The results of this work are compared with those from other recent methods, leading to the conclusion that our algorithm is comparable with other solutions, while approximating the average accuracy of a human observer without a significant degradation of sensitivity and specificity, with significant improvement in processing time.