Analysis of the shape and location of abrasive grain tips as well as their changes during the grinding process, is the basis for forecasting the machining process results. This paper presents a methodology of using the watershed segmentation in identifying abrasive grains on the abrasive tool active surface. Some abrasive grain tips were selected to minimize the errors of detecting many tips on a single abrasive grain. The abrasive grains, singled out as a result of the watershed segmentation, were then analyzed to determine their geometric parameters. Moreover, the statistical parameters describing their locations on the abrasive tool active surface and the parameters characterizing intergranular spaces were determined.

Retinitis pigmentosa is a genetic disorder that results in nyctalopia and its progression leads to complete loss of vision. The analysis and the study of retinal images are necessary, so as to help ophthalmologist in early detection of the retinitis pigmentosa. In this paper fundus images and Optical Coherence Tomography images are comprehensively analyzed, so as to obtain the various morphological features that characterize the retinitis pigmentosa. Pigment deposits, important trait of RP is investigated. Degree of darkness and entropy are the features used for analysis of PD. The darkness and entropy of the PD is compared with the different regions of the fundus image which is used to detect the pigments in the retinal image. Also the performance of the proposed algorithm is evaluated by using various performance metrics. The performance metrics are calculated for all 120 images of RIPS dataset. The performance metrics such as sensitivity, sensibility, specificity, accuracy, F-score, equal error rate, conformity coefficient, Jaccard’s coefficient, dice coefficient, universal quality index were calculated as 0.72, 0.96, 0.97, 0.62, 0.12, 0.09, 0.59, 0.45 and 0.62, respectively.