1,721,283 research outputs found
Robust segmentation and measurements techniques of white cells in blood microscope images
No full textThe analysis and the count of blood cell in microscope image can provide useful information concerning the health of the patients. In particular, morphological analysis of white cell deformations can effectively detect important diseases such as the Acute Lymphoblastic Leukemia. Blood images obtained by microscopes coupled with a digital camera are simple to obtain and can be more simply transmitted to clinical centers than liquid blood samples. Automatic measurement systems for white cells in blood microscope image can greatly help blood experts that typically inspect blood films manually.
Unfortunately, the analysis made by human experts is not rapid and it presents a not standardized accuracy due to the operator’s capabilities and tiredness. The presented paper shows how that it is effectively possible to accurately measure the white cells properties in order to allow, at a second stage, the leukemia identification. In particular, the paper presents how to suitably enhance the microscope image by removing the undesired microscope background and a new segmentation strategy to robustly identify white cells permitting to better extract their features for subsequent automatic diagnosis of diseases
Computational intelligence techniques for reflections identification in iris biometric images
No full textIris biometric systems identify individuals by comparing the characteristics of the iris acquired by the acquisition sensors. When reflections are present in the iris image, the portion of the image covered by reflections must be discarded from any further comparison since it can produce false matches. The paper presents a methodology for reflections identification in iris biometric images based on neural networks. In particular, this paper proposes a set of features which can be extracted from the iris image and that can be effectively used to achieve an accurate identification of the reflection position using a neural network Moreover, the paper presents how the radial symmetry operator can be used as a proper feature to identify the reflections in iris images. The method is general and can be used in any biometric system based on iris images
Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images
No full textThe early identification of acute lymphoblastic leukemia symptoms in patients can greatly increase the probability of recovery. Nowadays the leukemia disease can be identified by automatic specific tests such as Cytogenetics and Immunophenotyping and morphological cell classification made by experienced operators observing blood/marrow microscope images.
Those methods are not included into large screening programs and are applied only when typical symptoms appears in normal blood analysis. The Cytogenetics and Immunophenotyping diagnostic methods are currently preferred for their great accuracy with respect to the method of blood cell observation which presents
undesirable drawbacks: slowness and it presents a not standardized accuracy since it depends on the operator’s capabilities and tiredness. Conversely, the morphological analysis just requires an image -not a blood sample- and hence is suitable for low-cost and remote diagnostic systems. The presented paper shows the effectiveness of an automatic morphological method to identify the Acute Lymphocytic Leukemia by peripheral blood microscope images. The proposed system firstly individuates in the blood image the leucocytes from the others blood cells, then it selects the lymphocyte cells (the ones interested by acute leukemia), it evaluates morphological indexes from those cells and finally it classifies the presence of the leukemia
Special section in IEEE transactions on instrumentation and measurement on biometric instrumentation and measurement [Guest editorial]
No full textA 'combination of multiple classifier' design for low-complex, highly performing and power-aware classifiers
No full textIn this paper we study the relationships among the Combination of Multiple Classifier design philosophy, application level properties such as temporal and spatial locality of the inputs and low level aspects immediately impacting on power consumption, cache miss and computational complexity reduction. The CMC structure requires a set of independent simple sub-classifiers, each of which ruling in an application sub-domain under the control of a master enabling module and is particularly appealing in embedded system implementation. Only a sub-classifier is active at a time, the others being switched off
Adaptive reflection detection and location in iris biometric images by using computational intelligence techniques
No full textIris-based biometric systems identify individuals by comparing the characteristics of the iris captured by suited sensors. When reflections are present in the iris image, the portion of the iris covered by the reflections should not be considered in the comparison since it may produce erroneous matches. This paper presents an adaptive design methodology for reflection detection and location in iris biometric images based on inductive classifiers, such as neural networks. In particular, this paper proposes a set of features that can be extracted and measured from the iris image and that can effectively be used to achieve an accurate identification of the reflection position using a trained classifier. In addition, the use of radial symmetry transform (RST) is presented to identify the reflections in iris images. The proposed design methodology is general and can be used in any biometric system based on iris images
Implementations of computational intelligence techniques
No full textComputational Intelligence techniques are a powerful and adaptable approach to tackle problems and cases for which the conventional technologies have not been proved sufficiently effective. These results are achieved by mimicking some aspects of the knowledge representation and processing performed by the brain. The computational efforts implied by these approaches are usually quite relevant
Design of an automatic wood types classification system by using fluorescence spectra
No full textThe classification of wood types is needed in many industrial sectors, since it can provide relevant information concerning the features and characteristics of the final product (appearance, cost, mechanical properties, etc.). This analysis is typical in the furniture industries and the wood panel production. Usually, the analysis is performed by human experts, is not rapid, and has a nonuniform accuracy related mainly to the operators experience and attention. This paper presents a methodology to effectively cope with the design of an automatic wood types classification system based on the analysis of the fluorescence spectra suitable for real-time applications. This paper presents an experimental set up based on a laser source, a spectrometer, and a processing system, and then, it discusses a set of techniques suitable to extract features from the spectra and how to exploit the extracted feature to train an inductive classification system capable to properly classify the wood types. Obtained experimental results show that the proposed approach can achieve a good accuracy in the classification and requires a limited computational power, hence allowing for the application in real-time industrial processes
Morphological classification of blood leucocytes by microscope images
No full textThe classification and the count of white blood cells in microscopy images allows the in vivo assessment of a wide range of important hematic pathologies (i.e., from presence of infections to leukemia). Nowadays, the morphological cell classification is typically made by experienced operators. Such a procedure presents undesirable drawbacks: slowness and it presents a not standardized accuracy since it depends on the operator's capabilities and tiredness. Only few attempts of partial/full automated systems based on image-processing systems are present in literature and they are still at prototype stage. This paper presents a methodology to achieve an automated detection and classification of leucocytes by microscope color images. The proposed system firstly individuates in the blood image the leucocytes from the others blood cells, then it extracts morphological indexes and finally it classifies the leucocytes by a neural classifier in Basophil, Eosinophil, Lymphocyte, Monocyte and Neutrophil
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