Retinal blood vessel segmentation using random forest Gabor feature selection and automatic thresholding.
Successful computer aided diagnosis of ocular diseases is normally dependent on the accurate detection of components such as blood vessels, optic disk, fovea and microaneurysms. The properties of these components can be indicative of the presence and/or severity of pathology. Since most prevalent forms of ocular diseases emanate from vascular disorders, it is expected that accurate detection of blood vessels is essential for ocular diagnosis. In this research work, we investigate several opportunities for improvement of retinal blood vessel segmentation with the hope that they will ultimately lead to improvement in the diagnosis of vascular related ocular diseases. We complement existing work in this domain by introducing new Gabor lter features and selecting the most e ective of these using Random Forests feature selection. The actual segmentation of blood vessels is then done using an improved automatic thresholding scheme based on the preferred Gabor feature. We propose Random Forest (RF) feature ranking algorithms that demonstrate reliable feature set partitions over several University of California, Irvine (UCI) datasets. To circumvent instances of unreliable rankings, we also propose feature rank and RF strength correlation as an alternative indicator. Of the four proposed Gabor features, the maximum magnitude response is con rmed as the most e ective, as is the general trend in previous literature. The proposed Selective Valley Emphasis thresholding technique achieves identical segmentation results to the legacy approach while improving on computational e ciency. Sensitivity and speci city outcomes of up to 76.8% and 97.9% as well as 78.8% and 97.8% are achieved on the DRIVE and STARE datasets, respectively.
Doctoral Degree. University of KwaZulu-Natal, Durban.