No abstract

Rosai-Dorfman disease (RDD) is a rare condition similar to lymphomas, presenting with cervical lymphadenopathy in young adults. Extra-nodal involvement is relatively common but involvement of the central nervous system (CNS) is rare. Cranial RDD presents with symptoms of raised intracranial pressure, focal or generalised seizures, while spinal RDD presents with pain, peripheral neurological deficits and radiculopathy. In contrast to other similar neoplastic or degenerative conditions affecting the CNS, RDD is a benign, non-infective, granulomatous disorder. Radiologically cranio-spinal RDD often mimics commoner dural-based lesions like meningioma, with only subtle radiological differentiating findings on Magnetic Resonance Imaging (MRI). The histopathology of RDD is diagnostic. Surgical excision is preferred modality of treatment. However, adjuvant therapies like steroids and radiation may help controlling residual or recurrent disease. There are multiple sporadic reports and short case publications in the literature, often focusing on a particular aspect of RDD. In this study, authors aim to present five cases of craniospinal RDD, and comprehensive review of literature and highlight neurological complications of systemic RDD.

In diabetic patients, the chances of vision loss are higher. These issues related to vision can be diagnosed using diabetic retinopathy. It is one of the very important diseases amongst all retinal pathologies. One of the simplest changes observed on the eye due to diabetes is lesions in yellow or white color i.e. hard exudates (EX). It appears bright in fundus images and hence it is the most important to detect using image processing algorithm. In this work the proposed algorithm used is based on morphological feature extraction. Post processing techniques are required to separate out EX from other bright artefacts such as cotton wool spot and optic disc. The performance evaluation of the proposed algorithm shows the sensitivity of 96.7%, specificity 85.4% and accuracy of 91% on image level detection on Diaretdb1 database and achieved higher accuracy on publicly available e-ophtha EX retinal image database in terms of lesion level detection. It is computationally efficient as an automated system to assist the ophthalmologist. Early detection of hard exudates is crucial for diagnosing the stages of diabetic retinopathy to prevent blindness.

Aim: Fundus image analysis is the basis for the better understanding of retinal diseases which are found due to diabetes. Detection of earlier markers such as microaneurysms that appear in fundus images combined with treatment proves beneficial to prevent further complications of diabetic retinopathy with an increased risk of sight loss. Methods: The proposed algorithm consists of three modules: (1) image enhancement through morphological processing; (2) the extraction and removal of red structures, such as blood vessels preceded by detection and removal of bright artefacts; (3) finally, the true microaneurysm candidate selection among other structures based on feature extraction set. Results: The proposed strategy is successfully evaluated on two publicly available databases containing both normal and pathological images. The sensitivity of 89.22%, specificity of 91% and accuracy of 92% achieved for the detection of microaneurysms for Diaretdb1 database images. The algorithm evaluation for microaneurysm detection has a sensitivity of 83% and specificity 82% for e-ophtha database. Conclusion: In automated detection system, the successful detection of the number of microaneurysms correlates with the stages of the retinal diseases and its early diagnosis. The results for true microaneurysm detection indicates it as a useful tool for screening colour fundus images, which proves time saving for counting of microaneurysms to follow Diabetic Retinopathy Grading Criteria.

The principal target of preprocessing is to get more appropriate resultant image than its original for further additional analysis. Enhancement of retinal images creates several challenges. The main obstacle is to develop a technique to accommodate the wide variation in contrast inside the image. Necessity of preprocessing methods are for image normalization and to increase the contrast for achieving accurate analysis. This work examined literature in the prior process of digital imaging, in the field of the analysis of fundus image to extract normal and pathologic retinal traits within the context of diabetic retinopathy (DR).