Novel image processing techniques to detect lesions using lab view

Automated analysis of retinal images can assist in the diagnosis and management of blinding retinal diseases, such as diabetic retinopathy, age-related macular degeneration (AMD), and glaucoma. For evaluating and imaging patients with retinal diseases, clinical photographers usually capture color images of the retina using a specialized fundus camera. Subsequently, a fluorescein dye is injected into a vein in the subject's arm, and as the dye propagates through the retinal blood vessels, and series of (over a 5–10 min period) pictures of the retina are taken. Retinal fluorescein images at two different stages of Angiogram and a color fundus image of the same patient are used as input images for lesion diagnosis. From the two fluorescein images, vessel extraction is done, they are aligned and fused to identify the region of abnormality and lesion growth. From the color image, the 3D plot is simulated to identify the area of abnormality of the eye and is used as reference to the fused fluorescein image. In this paper we present a novel technique for diagnosis of lesions through Fluorescein Angiographic Images using Virtual Instrumentation(VI).

[1]  T J Naduvilath,et al.  Optic disc size in ocular hypertension. , 1999, Indian journal of ophthalmology.

[2]  I. Deary,et al.  Retinal image analysis: Concepts, applications and potential , 2006, Progress in Retinal and Eye Research.

[3]  Pascale Massin,et al.  Automatic detection of microaneurysms in color fundus images , 2007, Medical Image Anal..

[4]  Jacob Scharcanski,et al.  A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images , 2010, Comput. Medical Imaging Graph..

[5]  Heinrich Niemann,et al.  Automated segmentation of the optic nerve head for diagnosis of glaucoma , 2005, Medical Image Anal..

[6]  Dogan Aydin,et al.  Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm , 2009, Comput. Methods Programs Biomed..

[7]  D. Okada,et al.  Digital Image Processing for Medical Applications , 2009 .

[8]  Hiroshi Fujita,et al.  Detection of retinal nerve fiber layer defects in retinal fundus images using Gabor filtering , 2007, SPIE Medical Imaging.

[9]  Nicholas Ayache,et al.  Medical Image Analysis: Progress over Two Decades and the Challenges Ahead , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Gérard G. Medioni,et al.  2-D registration and 3-D shape inference of the retinal fundus from fluorescein images , 2008, Medical Image Anal..

[11]  Hong Shen,et al.  Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms , 1999, IEEE Transactions on Information Technology in Biomedicine.