Automated Diagnosis System for Age-Related Macular Degeneration Using Hybrid Textural Features Set from Fundus Images

Impairment to macula can cause loss of central vision. There are various macular disorders that can affect macular region and if not treated at an early stage can cause irreversible central vision loss, Age-Related Macular Degeneration (ARMD) disorder is one of the most threading macular disorder. Bright lesion, drusens presence in macular region is known as the hall mark of ARMD disorder. This bright lesion differentiation from other bright lesion like exudates is important for accurate diagnosis of ARMD. Focus of this paper is automated diagnosis of effected macular region by applying a hybrid textural feature set for more accurate detection of ARMD at an early stage using fundus images. These features also help to distinguish drusens from exudates. The proposed algorithm at first detect macular region from input fundus images and then perform features extraction based on textural pattern, edge and structural properties of macular region to classify abnormal macula from normal macula. Proposed algorithm is tested on publicly available STARE and locally available AFIO datasets. Attained sensitivity, specificity and accuracy of our proposed system are 97.5%, 95% and 95.52% respectively, when applied on STARE dataset. When we have applied our proposed system on AFIO dataset we have attained sensitivity, specificity and accuracy of 98%, 92% and 92.31% respectively.