Towards Complete Ocular Disease Diagnosis in Color Fundus Image

Non-invasive assessment of retinal fundus image is well suited for early detection of ocular disease and is facilitated more by advancements in computed vision and machine learning. Most of the Deep learning based diagnosis system gives just a diagnosis(absence or presence) of a certain number of diseases without hinting the underlying pathological abnormalities. We attempt to extract such pathological markers, as an ophthalmologist would do, in this thesis and pave a way for explainable diagnosis/assistance task. Such abnormalities can be present in various regions of a fundus image including vasculature, Optic Nerve Disc/Cup, or even in non-vascular region. This thesis consist of series of novel techniques starting from robust retinal vessel segmentation, complete vascular topology extraction, and better ArteryVein classification. Finally, we compute two of the most important vascular anomalies-arteryvein ratio and vessel tortuosity. While most of the research focuses on vessel segmentation, and artery-vein classification, we have successfully advanced this line of research one step further. We believe it can be a very valuable framework for future researcher working on automated retinal disease diagnosis

Retinal Blood Vessel Extraction from Fundus Images Using Enhancement Filtering and Clustering

Screening of vision troubling eye diseases by segmenting fundus images eases the danger of loss of sight of people. Computer assisted analysis can play an important role in the forthcoming health care system universally. Therefore, this paper presents a clustering based method for extraction of retinal vasculature from ophthalmoscope images. The method starts with image enhancement by contrast limited adaptive histogram equalization (CLAHE) from which feature extraction is accomplished using Gabor filter followed by enhancement of extracted features with Hessian based enhancement filters. It then extracts the vessels using K-mean clustering technique. Finally, the method ends with the application of a morphological cleaning operation to get the ultimate vessel segmented image. The performance of the proposed method is evaluated by taking two different publicly available Digital retinal images for vessel extraction (DRIVE) and Child heart and health study in England (CHASE_DB1) databases using nine different performance matrices. It gives average accuracies of 0.952 and 0.951 for DRIVE and CHASE_DB1 databases, respectively.    

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

Towards non-vascular fundus image analysis and disease detection

Assessment of retinal fundus image is very informative and preventive in early ocular disease detection. This non-invasive assessment of fundus images also helps in the early diagnosis of vascular diseases. This unique combination help in the early diagnosis of diseases. Applying image enhancement techniques with advanced Deep learning techniques helps to overcome such a challenging problem. Most Deep learning models give a diagnosis without attention to underlying pathological abnormalities. In this thesis, we tried to solve the problem in the same way as ophthalmologists and experts in the field approach the problem. We created models that can detect an Optic disc, Optic cup, and vascular regions in the image. This work can be integrated into any ocular disease detection, such as glaucoma, and vascular disease detection, such as diabetes. Extensive work is applied for better sampling when all models were suffering from a lack of data in the medical imaging field. The entire work on the retinal fundus image was in 2d images. In the extension of this work, we applied our knowledge to 3d MRI-Brain images. We attempt to predict attention scores in children, which is a big factor in the detection of kids with ADHD. But both work on fundus images and brain MRI images are under the umbrella of medical imaging. We believe this advancement in this line of research can be very valuable for future researchers in the area of automated medical imaging, especially in automated retinal disease diagnosis