I will present an overview of the capabilities of ImageJ both as an image processing tool, as a basis for ever more sophisticated plug-ins, as well as a library of basic image processing functions for complete applications including computer aided diagnosis. ImageJ will be briefly demonstrated, as well as functionality will be shown applying to volume rendering, lesion detection, registering, mosaicing and stitching of images, as well as less or more sophisticated intensity and contrast corrections.

No abstract

Purpose A comparison of thickness measurements of segmented spectral domain optical coherence tomography (OCT) derived topographic maps of patients with no or minimal diabetic retinopathy (DR) versus healthy controls. Methods Ninety‐nine patients, 44 with type 1 DM, and 36 with type 2 DM, with no or minimal DR underwent full ophthalmic examination, fundus photography and spectral domain OCT (3D OCT‐1000, Topcon). Following automated segmentation the mean thickness was calculated for 6 layers: 1/ Retinal Nerve Fibre Layer (RNFL), 2/ Ganglioncell layer (GCL) + Inner Plexiform Layer (IPL), 3/ Inner Nuclear Layer, 4/ Outer Plexiform Layer, 5/ Outer Nuclear Layer + Inner Segments (photoreceptor), 6/ Outer Segments (photoreceptor), in the ETDRS defined regions of the macula and compared to 76 age and sex matched healthy controls. Results The total retinal thickness in the diabetic patients was reduced compared to the healthy controls. In the diabetic patients both the mean RNFL and the mean OPL were significantly (p<0.05) thinner. Conclusion The decreased total retinal thickness in diabetic patients with no or minimal retinopathy may be due to a selective loss of thickness in several retinal layers and supports the concept of early DR as a neuro‐degenerative disease.