Segmentation of blurred objects by classification of isolabel contours

• The algorithm is suitable for segmentation of grayscale blurred homogeneous objects.
• The objects are segmented by SVM classification of isolabel contours.
• Labeling training images is reduced to specifying contours on isolabel-contour map.
• Classification of isolabel contours allows using of global object features.
• High performance is demonstrated for segmentation of brain lesions in MRI.

Segmentation of objects with blurred boundaries is an important and challenging problem, especially in the field of medical image analysis. A new approach to segmentation of homogeneous blurred objects in grayscale images is described in this paper. The proposed algorithm is based on building of an isolabel-contour map of the image and classification of closed isolabel contours by the SVM. Each closed isolabel contour is described by the feature vector that can include intensity-based features of the image area enclosed by the contour, as well as geometrical features of the contour shape. The image labeling procedure for construction of the training base becomes very fast and convenient because it is reduced to clicking on isolabel contours delineating the objects of interest on the isolabel-contour map. The proposed algorithm was applied to the problem of brain lesion segmentation in MRI and demonstrated performance figures above 98% on real data, both in sensitivity and in specificity.