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The analysis of digital pathology images, catalyzes research and automate diagnosis for improving clinical care. Regardless of the advances in high-resolution and speedy scanning devices, the clinical analysis of Whole Slide Image (WSI) remains mainly as the work of an image analyst. The complications when working on these images are primarily, its enormous image size, as well as the heterogeneous nature of tissue regions. These reasons may lead to inefficient computer analysis. We propose a comprehensive algorithm to analyse, these large images by using small tile image analysis. The small tile images can be learnt individually in detail, rather than processing the whole high resolution WSI. The analysis of these small tile images helps us configure, the localised pathology image characteristics. The work flow involves extracting Perceptual Features (PF), Fourier Local Binary Patterns (FLBP), Histogram, Gabor and Gray Level Co-occurrence Matrix (GLCM) features from the tiled WSI. Then, the multidimensional feature set is provided to the classifiers like K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB) and Logistic Regression (LR) for performance analysis. We evaluated our predictive models by automatically classifying image datasets (186 and 124) of brain tumour cases, using 10 fold cross validation. The model classified the images into Low Grade Glioma (LGG) and Glioblastoma Multiforme (GBM). It gives the highest accuracy of 93.5% for a feature combination of PF+FLBP+GLCM+GABOR with linear Support Vector Machine (linSVM) and Area under the Curve (AUC) of 0.9741 for the 186 image dataset. Also linSVM gives an accuracy of 96.8% with various feature combinations for the 124 image dataset. We did an exhaustive study on the performance of various sets of texture descriptors and classification models. This resulted in finding an optimised classification approach with a combined feature set that outperformed the original feature set classification. The proposed work is unique because it combines a wide range of feature extracting methods and an exhaustive list of classifiers showcasing the best method. The idea that, rather than selecting a region of interest from WSI for grade designation, all the regions in a slide are considered. This enables the involvement of each tile in making the final diagnostic decision, considering the heterogeneous nature of WSI. Our work can be extended for different tumour subtypes. Also, the experiment can be scaled with different sample sizes and finally the method works without a pathologist input. [ABSTRACT FROM AUTHOR]

Phytoplankton species composition and distribution in relation to environmental parameters was investigated from the inshore and coastal waters of New Mangalore Port. A total of 16 stations were selected in such a way that 3 were inside the port and 13 in near shore coastal waters. Water quality parameters such as temperature, salinity, pH, dissolved oxygen along with nutrients such as nitrate, phosphate and silicate were monitored; along with phytoplankton biomass (chlorophyll a) and species composition. Chlorophyll a ranged between 0.01 and 4.95 mg m-3 among the stations. Cumulative abundance of phytoplankton was mainly dominated by diatoms (63 %), followed by dinoflagellates (24 %) whereas average number of phytoplankton are more observed in station 4 followed by station 6 and station 1. The present study infers that the phytoplankton diversity and distribution are subject to changes in physico-chemical conditions of the ecosystem. Therefore, a frequent monitoring of these ecosystems with its physico-chemical conditions and biological diversity provides knowledge of the health of ecosystem. [ABSTRACT FROM AUTHOR]