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Image representation is one of the major aspects of automatic classification algorithms. In this paper, different feature extraction techniques have been utilized to represent medical X-ray images. They are categorized into two groups; (i) low-level image representation such as Gray Level Co-occurrence Matrix(GLCM), Canny Edge Operator, Local Binary Pattern(LBP) , pixel value, and (ii) local patch-based image representation such as Bag of Words (BoW). These features have been exploited in different algorithms for automatic classification of medical Xray images. We then analyzed the classification performance obtained with regard to the image representation techniques used. These experiments were evaluated on ImageCLEF 2007 database consists of 11000 medical X-ray images with 116 classes. Experimental results showed the classification performance obtained by exploiting LBP and BoW outperformed the other algorithms with respect to the image representation techniques used.

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Content-based image retrieval techniques have been extensively studied for the past few years. With the growth of digital medical image databases, the demand for content-based analysis and retrieval tools has been increasing remarkably. Blood cell image is a key diagnostic tool for hematologists. An automated system that can retrieved relevant blood cell images correctly and efficiently would save the effort and time of hematologists. The purpose of this work is to develop such a content-based image retrieval system. Global color histogram and wavelet-based methods are used in the prototype. The system allows users to search by providing a query image and select one of four implemented methods. The obtained results demonstrate the proposed extended query refinement has the potential to capture a user's high level query and perception subjectivity by dynamically giving better query combinations. Color-based methods performed better than wavelet-based methods with regard to precision, recall rate and retrieval time. Shape and density of blood cells are suggested as measurements for future improvement. The system developed is useful for undergraduate education.

The demand for automatically classification of medical X-ray images is rising faster than ever. In this paper, an approach is presented to gain high accuracy rate for those classes of medical database with high ratio of intraclass variability and interclass similarities. The classification framework was constructed via annotation using the following three techniques: annotation by binary classification, annotation by probabilistic latent semantic analysis, and annotation using top similar images. Next, final annotation was constructed by applying ranking similarity on annotated keywords made by each technique. The final annotation keywords were then divided into three levels according to the body region, specific bone structure in body region as well as imaging direction. Different weights were given to each level of the keywords; they are then used to calculate the weightage for each category of medical images based on their ground truth annotation. The weightage computed from the generated annotation of query image was compared with the weightage of each category of medical images, and then the query image would be assigned to the category with closest weightage to the query image. The average accuracy rate reported is 87.5 %.

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Monitoring water quality is a critical aspect of environmental sustainability. Poor water quality has an impact not just on aquatic life but also on the ecosystem. The purpose of this systematic review is to identify peer-reviewed literature on the effectiveness of applying machine learning (ML) methodologies to estimate water quality parameters with satellite data. The data was gathered using the Scopus, Web of Science, and IEEE citation databases. Related articles were extracted, selected, and evaluated using advanced keyword search and the PRISMA approach. The bibliographic information from publications written in journals during the previous two decades were collected. Publications that applied ML to water quality parameter retrieval with a focus on the application of satellite data were identified for further systematic review. A search query of 1796 papers identified 113 eligible studies. Popular ML models application were artificial neural network (ANN), random forest (RF), support vector machines (SVM), regression, cubist, genetic programming (GP) and decision tree (DT). Common water quality parameters extracted were chlorophyll-a (Chl-a), temperature, salinity, colored dissolved organic matter (CDOM), suspended solids and turbidity. According to the systematic analysis, ML can be successfully extended to water quality monitoring, allowing researchers to forecast and learn from natural processes in the environment, as well as assess human impacts on an ecosystem. These efforts will also help with restoration programs to ensure that environmental policy guidelines are followed.