Background: Malaria parasitemia is commonly used as a measurement of the amount of parasites in the patient's blood and a crucial indicator for the degree of infection. Manual evaluation of Giemsa-stained thin blood smears under the microscope is onerous, time consuming and subject to human error. Although automatic assessments can overcome some of these problems the available methods are currently limited by their inability to evaluate cases that deviate from a chosen "standard" model.
A technique is proposed for estimating parasitaemia from blood smear images by extracting healthy and parasite infected red blood cells. The developed approach accounts for uncertain imaging conditions due to microscope settings as well as the quality of the blood smear preparation. The solution is based on a multi-stage estimation process with minimal prior knowledge starting from a model representation of red blood cells. Based on pattern matching with parameter optimisation and cross-validation against the expected biological characteristics, red blood cells are determined. In a final stage, the parasitaemia measure is carried out by partitioning the uninfected and infected cells using an unsupervised and in comparison a training-based technique. Finally, the obtained estimates were analysed with respect to manually acquired results from professionals. Red blood cells detection resulted in precision and recall rates of 80-88% and 92-98%, respectively. By using a trainingbased method, the precision and recall rates were improved to 92% and 95%, respectively.
Content-based techniques enable retrieval of remotely sensed data based on low-level features. However, the deep gap between low-level features and high-level semantics concepts is a major obstacle to more effective image retrieval. Therefore, a semantics-based retrieval approach was implemented. The semantics classifiers are trained using heterogeneous features from a group of satellite images. The proposed approach is mainly composed of two steps. The first step is to form hierarchical semantics classifiers based on the low-level features of the training images. In the second step, unknown satellite images are classified into a certain semantics class if their feature vectors are located in the corresponding feature space. To achieve an effective and at the same time efficient identification of the multiple semantics classes within the satellite scenes, an approximation approach was developed. At the query time, the system retrieves the satellite images based on semantics classes extracted from the query image or provided directly by the users.
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