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Diabetic retinopathy (DR) is a condition that damages the retina in people with diabetes and can lead to vision loss. It can be detected by observing the morphological changes in the fundus image. DR manifests in several types, including, mild non-proliferative DR (MilNPDR), moderate non-proliferative DR (ModNPDR), severe non-proliferative DR (SevNPDR), proliferative DR (PDR), and Severe proliferative DR (SPDR). Diagnosing DR manually is a time-consuming process and requires significant human resources. Computational-based diagnostic framework, on the other hand, may facilitate early detection and timely treatments. Diabetes during pregnancy can lead to a future development of DR in the mother as well as the fetus. The effect of pregnancy on DR is a significant health concern, however, it is unexplored due to the limited number of screening and treatment options. This manuscript aims to give a comprehensive review of DR and its correlation with diabetes during pregnancy. Moreover, it presents the available resources of fundus data sets for DR research and depicts the advanced methodologies for computational aided DR detection. It also highlights the research gaps in DR detection and its classification and therefore opens the possibility of future research on DR during pregnancy. [ABSTRACT FROM AUTHOR]

Clustering is one of the important unsupervised learning in data mining to group the similar features. The growing point of the cluster is known as a seed. To select the appropriate seed of a cluster is an important criterion of any seed based clustering technique. The performance of seed based algorithms are dependent on initial cluster center selection and the optimal number of clusters in an unknown data set. Cluster quality and an optimal number of clusters are the important issues in cluster analysis. In this paper, the proposed seed point selection algorithm has been applied to 3 band image data and 2D discrete data. This algorithm selects the seed point using the concept of maximization of the joint probability of pixel intensities with the distance restriction criteria. The optimal number of clusters has been decided on the basis of the combination of seven different cluster validity indices. We have also compared the results of our proposed seed selection algorithm on an optimal number of clusters using K-Means clustering with other classical seed selection algorithms applied through K-Means Clustering in terms of seed generation time (SGT), cluster building Time (CBT), segmentation entropy and the number of iterations (NOTK−means). We have also made the analysis of CPU time and no. of iterations of our proposed seed selection method with other clustering algorithms. [ABSTRACT FROM AUTHOR]

Image segmentation, the splitting of a multispectral and panchromatic image into groups of homogeneous pixels based on the region of interest(ROI), is a universal step for many advanced image processing and object recognition. Image segmentation essentially affects the overall performance of any automated image analysis system due to utmost importance of its quality. Image segmentation can be performed by recursively splitting the whole image or by merging together a large number of minute regions until a specified condition is satisfied. Thresholding is an old, simple and important method in gray scale image segmentation. In this paper, we have used Shannon’s entropy and proposed a new multilevel thresholding image segmentation method based on minimization of bi-entropy function. A smoothing technique based on weight value of the pixel within a w × w moving window is introduced to make the splitting result continuous and qualitative. The proposed algorithm takes full account of the spatial information and the gray information to decrease the computing quantity. Standard medical images, texture images, and remote sensing images are segmented in the experiment and compared with other related segmentation methods with different measures. Experimental results show that the proposed method can quickly converge with high computational efficiency. [ABSTRACT FROM AUTHOR]