Hybrid Nature Inspired SMO-GBM Classifier for Exudate Classification on Fundus Retinal Images

Background: The diabetic retinopathy can result in loss of vision if not detected in the earlier stages. Exudates are the lesions which play a crucial role in early diagnosis of diabetic retinopathy. The localization of exudates lesions with high values of performance metrics is complicated due to presence of blood vessels and other noisy artifacts. Method: We present computer aided system for classification of retinal fundus images using a novel nature inspired spider monkey optimization for parameter tuning of gradient boosting machines classifier. The image enhancement has been performed with histogram equalization and contourlet transform. The pixels belonging to optic disc region are detected and eliminated using circular Hough transform and Otsu's segmentation method. We have employed Kirsch's matrices for blood vessel detection. The GLCM based feature vector extraction has been employed for textural features. The classification has been performed with hybrid SMO-GBM classifier. Result: We have utilized the STARE database for validation of proposed technique. The proposed system can effectively classify entire image set from test data. The SMO-GBM classifier can further sub-segregate into sub classes with an average accuracy of 97.5%. Conclusion: The proposed approach provides detection and grading of diabetic retinopathy. The abnormality is further categories as soft, moderate and severe. The hybrid SMO-GBM classifier yields a better statistical metrics than the existing exudates classification approaches.     

Degenerated boutons in the fundus striati (nucleus accumbens septi) after lesion of the parafascicular nucleus in the cat

Summary An attempt has been made to reveal which of the nine different types of synapses in the fundus striati, discriminated in a previous study, degenerate following experimental lesions in the parafasciculo-center median complex of the cat. Two types of synaptic contacts were found to be degenerated two days after the lesion was performed: (1) the axo-spinous type IV, characterized by densely-packed, small, round vesicles and a strong asymmetric thickening, and, (2) the axo-dendritic or axo-somatic type VII, again characterized by small, round vesicles in a dense accumulation and an asymmetric thickening. After two days of survival the original characteristics of the boutons could still be recognized in both types of synapses.A positive correlation exists between the location and extent of the coagulation foci in the parafascicular nucleus and the appearance of degenerated boutons in the fundus striati. Therefore, the conclusion that the parafasciculofundus neurons terminate as type IV or type VII boutons is entirely justified. Additionally, the role of the special types of boutons in the synaptic organization of the fundus striati has beeen discussed.Dr. J. W. Chung on leave of absence from the Department of Anatomy, Catholic Medical College, Seoul, KoreaHerrn Professor Dr. Drs. h. c. Wolfgang Bargmann zum 70. Geburtstag gewidmet     

Occurrence of cilia in the fundus striati of the cat

Summary In the fundus striati of the cat a few multiple ciliated astroglial cells and a single-ciliated small neuron were observed. In astrocytes cilia display a 9+2 subfiber configuration. Acknowledgement. The authors are grateful to Prof. Dr. R. Hassler for his encouragement and valuable discussions     

Postnatal development of somatostatin levels and its binding sites in rabbit gastric mucosa

Using a specific radioimmunoassay technique, we have determined somatostatin-like immunoreactivity (SLI) in acid extracts of gastric (fundic and antral) mucosa as well as the specific binding of ¹²⁵I-Tyr¹¹-somatostatin to cytosol of the stomach of 0 to 150 days postnatal rabbits. The levels of somatostatin in both fundus and antrum decreased from birth up to day 5 followed by a sharp increase from 5 to 10 days, then decreased progressively until day 35. After this age, the somatostatin concentration remained relatively stable. The number of specific somatostatin binding sites of both high- and low-affinity increased gradually (without changes in the affinity values) with the development of rabbits, reaching the adult level by 35 days. However, there was an apparent lack of high-affinity sites immediately after birth (day 0). The somatostatin binding sites had characteristics identical with those found in adult animals with regard to their respective specific ligands.     

武汉市1060 例新生儿眼病筛查结果分析

目的:分析武汉市1060例新生儿眼病筛查结果,探讨新生儿眼病筛查的临床意义。方法:选择2014年1月至2014年12月在我院出生的1060例新生儿为研究对象,进行眼前节和眼底检查,并对检查结果进行分析。结果:1060例新生儿中发现眼前节异常者103例(9.72%),其中结膜下出血43例(4.06%),先天性泪囊炎44例(4.15%),先天性白内障5例(0.47%),先天性青光眼4例(0.38%),瞳孔异位7例(0.66%);眼底病变53例(5.00%),其中视网膜病变32例(3.02%),永存玻璃体动脉14例(1.32%),眼底出血4例(0.38%),脉络膜缺损3例(0.28%)。结论:新生儿眼病患病率较高。在新生儿出生后开展及时的眼部检查可以尽早发现新生儿眼病,有利于针对性治疗,进而降低后续眼部重症疾病的发生。     

Fundus Retinal Vessels Image Segmentation Method Based on Improved U-Net

ObjectivesAlthough the segmentation of retinal vessels in the fundus is of great significance for screening and diagnosing retinal vascular diseases, it remains difficult to detect the low contrast and the information around the lesions provided by retinal vessels in the fundus and to locate and segment micro-vessels in the fine-grained area. To overcome this problem, we propose herein an improved U-Net segmentation method NoL-UNet.Material and methodsThis work introduces NoL-UNet. First of all, the ordinary convolution block of the U-Net network is changed to random dropout convolution blocks, which can better extract the relevant features of the image and effectively alleviate the network overfitting. Next, a NoL-Block attention mechanism added to the bottom of the encoding-decoding structure expands the receptive field and enhances the correlation of pixel information without increasing the number of parameters.ResultsThe proposed method is verified by applying it to the fundus image datasets DRIVE, CHASE_DB1, and HRF. The AUC for DRIVE, CHASE_DB1 and HRF is 0.9861, 0.9891 and 0.9893, Se for DRIVE, CHASE_DB1 and HRF is 0.8489, 0.8809 and 0.8476, and the Acc for DRIVE, CHASE_DB1 and HRF is 0.9697, 0.9826 and 0.9732, respectively. The total number of parameters is 1.70M, and for DRIVE, it takes 0.050s to segment an image.ConclusionOur method is statistically significantly different from the U-Net method, and the improved method shows superior performance with better accuracy and robustness of the model, which has good practical application in auxiliary diagnosis.     

A Fast and Accurate Method for Glaucoma Screening from Smartphone-Captured Fundus Images

The glaucoma is an eye disease that causes blindness when it progresses in an advanced stage. Early glaucoma diagnosis is essential to prevent the vision loss. However, early detection is not covered due to the lack of ophthalmologists and the limited accessibility to retinal image capture devices.In this paper, we present an automated method for glaucoma screening dedicated for Smartphone Captured Fundus Images (SCFIs). The implementation of the method into a smartphone associated to an optical lens for retina capturing leads to a mobile aided screening system for glaucoma. The challenge consists in insuring higher performance detection despite the moderate quality of SCFIs, with a reduced execution time to be adequate for the clinical use. The main idea consists in deducing glaucoma based on the vessel displacement inside the Optic Disk (OD), where the vessel tree remains sufficiently modeled on SCFIs. Within this objective, our major contribution consists in proposing: (1) a robust processing for locating vessel centroids in order to adequately model the vessel distribution, and (2) a feature vector that relevantly reflect two main glaucoma biomarkers in terms of vessel displacement. Furthermore, all processing steps are carefully chosen based on lower complexity, to be suitable for fast clinical screening.A first evaluation of our method is performed using the two public DRISHTI-DB and DRIONS-DB databases, where 99% and 95% accuracy, 96.77% and 97,5% specificity and 100% and 95% sensitivity are respectively achieved. Thereafter, the method is evaluated using two fundus image databases respectively captured through a smartphone and retinograph for the same persons. We achieve 100% accuracy using both databases which assesses the robustness of our method. In addition, the detection is performed on 0.027 and 0.029 second when executed respectively on the Samsung-M51 on the Samsung-A70 smartphone devices. Our proposed smartphone app provides a cost-effective and widely accessible mobile platform for early screening of glaucoma in remote clinics or areas with limited access to fundus cameras and ophthalmologists.     

正常猕猴与人的视网膜电图比较

目的比较正常猕猴与人视网膜电图异同,为进一步利用猕猴建立动物模型研究视网膜疾病打下基础。方法健康成年猕猴7只（14只眼）与8例（16只眼）正常人进行视网膜电图检测,对两者Rod-ERG中的b波,Max-ERG的a、b波,Cone-ERG的b波峰时值及波幅和OPs的O2值,Flicker-ERG的P值进行统计学检验。结果猕猴与人的视网膜电图波形结果较为相似,各指标与人的结果相比,潜伏期短,幅值低,但Cone-ERG和Flicker-ERG两者幅值差异不具有统计学意义。结论视网膜电图检测从功能上证明猕猴较其他常用实验动物更接近人,尤其表现在视锥细胞及黄斑区功能,可用作人类视网膜疾病尤其是黄斑区病变的良好动物模型。