Comparative Study on Local Binary Patterns for Mammographic Density and Risk Scoring

Breast density is considered to be one of the major risk factors in developing breast cancer. High breast density can also affect the accuracy of mammographic abnormality detection due to the breast tissue characteristics and patterns. We reviewed variants of local binary pattern descriptors to classify breast tissue which are widely used as texture descriptors for local feature extraction. In our study, we compared the classification results for the variants of local binary patterns such as classic LBP (Local Binary Pattern), ELBP (Elliptical Local Binary Pattern), Uniform ELBP, LDP (Local Directional Pattern) and M-ELBP (Mean-ELBP). A wider comparison with alternative texture analysis techniques was studied to investigate the potential of LBP variants in density classification. In addition, we investigated the effect on classification when using descriptors for the fibroglandular disk region and the whole breast region. We also studied the effect of the Region-of-Interest (ROI) size and location, the descriptor size, and the choice of classifier. The classification results were evaluated based on the MIAS database using a ten-run ten-fold cross validation approach. The experimental results showed that the Elliptical Local Binary Pattern descriptors and Local Directional Patterns extracted most relevant features for mammographic tissue classification indicating the relevance of directional filters. Similarly, the study showed that classification of features from ROIs of the fibroglandular disk region performed better than classification based on the whole breast region

A Comparative Analysis of Antibiotic Resistance Pattern of Metallo-Β-Lactamase And Non-Metallo-Β-Lactamase Strains of Pseudomonas aeruginosa Isolated From Chronic Suppurative Otitis Media

BACKGROUND: Chronic suppurative otitis media (CSOM) is a notorious infection and a major health problem in developing countries causing serious local damage and threatening complications. Early and effective treatment based on the knowledge of causative micro organisms and their antimicrobial sensitivity ensures prompt clinical recovery and possible complications can thus be avoided. AIM OF THE STUDY: The aim of this study was to isolate the organisms associated with CSOM, to detect the antibiogram of the aerobic isolates, to study the prevalence of Metallo-beta-lactamase producing Pseudomonas aeruginosa and to compare the antibiotic resistance pattern of MBL producing and non-MBL producing Pseudomonas aeruginosa. MATERIALS AND METHODS: A total of 150 patients clinically diagnosed of CSOM were enrolled in the study and the samples were collected from the ENT department of Government Rajaji Hospital Madurai, from January 2015-june 2016 .The samples obtained from each patient using sterile cotton swabs and cultured for microbial flora. Drug susceptibility testing for aerobic isolates was conducted using Kirby Bauer disc diffusion method. Screening for MBL was done by Imipenem (10μg) disk and phenotypic confirmation of MBL was done by combined disk method. Genotypically confirmed by PCR. (blaVIM). RESULTS: The most common causative organisms isolated were Pseudomonas aeruginosa (56.44%), followed by Staphylococcus aureus(25.77%) amongst the 163 aerobic isolates The Metallo-beta-lactamase production among the Carbapnem resistant isolates were 6 (60 %) The percentage of Metallo-beta–lactamase production among Pseudomona aeruginosa was 6.52%. High level of drug resistance was observed for Gentamicin and Amikacin 83.33% TheMetallo-Beta-lactamase producing Pseudomonas aeruginosa were shown to exhibit more resistance than non metallo-beta-lactamase producing isolates. CONCLUSION: Knowing the etiological agents of CSOM and their antimicrobial susceptibility is of essential importance for an efficient treatment, prevention of both complications and development of antibiotic resistance and finally, the reduction of the treatment costs. Judicious use of antibiotics, rapid isolation of patients suspected to have Carbapenm resistant Pseudomonas aeruginosa infections and regular testing of all isolates for Metallo-beta-lactamase production among Pseudomonas aeruginosa is recommended for the prevention of tramission of Carbapenem resistant Pseudomonas aeruginosa

Mammographic Ellispe Modelling for Risk Estimation

AbstractIt has been shown that breast density and parenchymal patterns are significant indicators in mammographic risk assessment. In addition, studies have shown that the sensitivity of computer aided tools decreases significantly with increase in breast density. As such, mammographic density estimation and classification plays an important role in CAD systems. In this paper, we present the classification of mammographic images according to breast parenchymal structures through a multi-scale ellipse blob detection technique. Our classification is based on classifying the mammographic images of the MIAS dataset into high/low risk mammograms based on features extracted from a blob detection technique which is based on breast tissue structure. In addition, it evaluates the relation between the BIRADS classes and low/high risk mammograms. Results demonstrate the probability of estimating breast density using computer vision techniques to improve classification of mammographic images as low/high risk

Regional and seasonal variations in phytoplankton

One of the main goals of remote-sensing observations is the study of seasonal cycles of phytoplankton biomass in different regions of the World Ocean. In many regions these cycles repeat every year including minor details. This pattern is a result of seasonal oscillations of physical environment. In high latitudes these oscillations are more pronounced, and the response of phytoplankton is more evident

Satellite ocean colour sensors

The 70% of the earth’s surface is covered by the ocean and the life inhabiting the oceans play an important role in shaping the earth’s climate. Phytoplankton, also known as microalgae, are the single celled, autotrophic components of the plankton community and a key part of oceans, seas and freshwater basin ecosystems. They are significant factor in the ocean carbon cycle and, hence, important in all pathways of carbon in the ocean. Phytoplankton contain chlorophyll pigments for photosynthesis, similar to terrestrial plants and require sunlight in order to live and grow. Most of them are buoyant and float in the upper part of the ocean, where plenty of sunlight is available. They also require inorganic nutrients such as nitrates, phosphates, and sulphur which they convert into proteins, fats, and carbohydrates. In a balanced ecosystem, phytoplankton are the base of the food web and provide food for a wide range of sea creatures (NOAA). The measurement of phytoplankton can be indexed as chlorophyll concentration and is important as they are fundamental to understanding how the marine ecosystem responds to climate variability and climate change

Fundamentals of ocean colour remote sensing

Remote sensing refers to collection of information about an object without being in direct contact with the object. Remote sensing aids in measuring remote areas which are inaccessible by any other means and offer less expense than in-situ measurements. Remote sensing facilitates creation of long time series and extended measurement. This has the advantage that several parameters can be measured at same time and satellite-based remote sensing measurements allow global observations. Remote sensing has its own advantages and disadvantages. The limitation includes indirect measurements of large areas which are not of interest to the user. The automated instrument degradation creates retrieval errors and are affected by several factors/processes, and not only by the object of interest. Additional assumptions and models are needed for the interpretation of the measurements and before using these models in oceanographic studies, it is extremely important to validate the performance of the various ocean colour algorithms with in-situ observations (Swirgon et al., 2015)

Upwelling over the eastern Arabian Sea

Upwelling is a vital oceanographic phenomena determining the biological productivity of the coastal oceanic provinces in a greater extent. The annual pelagic fisheries of coastal rim countries, adjacent to the eastern boundary of the Ocean, over the trade wind zone are greatly dependent on upwelling. Over the North India Ocean (NIO), west coast of India, adjacent to the eastern Arabian Sea is well known for its seasonal occurrence of upwelling and downwelling annually. Over the past, several authors have studied upwelling along the west coast of India (Banse 1959, 1968; Sharma 1978; Johannessen et al., 1987