Fabio Scotti

Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images

The 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 operators 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.

All-IDB: The acute lymphoblastic leukemia image database for image processing

The visual analysis of peripheral blood samples is an important test in the procedures for the diagnosis of leukemia. Automated systems based on artificial vision methods can speed up this operation and increase the accuracy and homogeneity of the response also in telemedicine applications. Unfortunately, there are not available public image datasets to test and compare such algorithms. In this paper, we propose a new public dataset of blood samples, specifically designed for the evaluation and the comparison of algorithms for segmentation and classification. For each image in the dataset, the classification of the cells is given, as well as a specific set of figures of merits to fairly compare the performances of different algorithms. This initiative aims to offer a new test tool to the image processing and pattern matching communities, direct to stimulating new studies in this important field of research.

Morphological classification of blood leucocytes by microscope images

The 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 operators 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.

Noisy iris segmentation with boundary regularization and reflections removal

The paper presents an innovative algorithm for the segmentation of the iris in noisy images, with boundaries regularization and the removal of the possible existing reflections. In particular, the method aims to extract the iris pattern from the eye image acquired at the visible wavelength, in an uncontrolled environment where reflections and occlusions can also be present, on-the-move and at variable distance. The method achieves the iris segmentation by the following three main steps. The first step locates the centers of the pupil and the iris in the input image. Then two image strips containing the iris boundaries are extracted and linearizated. The last step locates the iris boundary points in the strips and it performs a regularization operation by achieving the exclusion of the outliers and the interpolation of missing points. The obtained curves are then converted into the original image space in order to produce a first segmentation output. Occlusions such as reflections and eyelashes are then identified and removed from the final area of the segmentation. Results indicate that the presented approach is effective and suitable to deal with the iris acquisition in noisy environments. The proposed algorithm ranked seventh in the international Noisy Iris Challenge Evaluation (NICE.I).

Composite real-time image processing for railways track profile measurement

Checking railway status is critical to guarantee high operating safety, proper maintenance schedule, low maintenance and operating costs. This operation consists of the analysis of the rail profile and level as well as overall geometry and undulation. Traditional detection systems are based on mechanical devices in contact with the track. Innovative approaches are based on laser scanning and image analysis. This paper presents an efficient composite technique for track profile extraction with real-time image processing. High throughput is obtained by algorithmic pre-filtering to restrict the image area containing the track profile, while high accuracy is achieved by neural reconstruction of the profile itself.

Privacy-Aware Biometrics: Design and Implementation of a Multimodal Verification System

A serious concern in the design and use of biometric authentication systems is the privacy protection of the information derived from human biometric traits, especially since such traits cannot be replaced. Combining cryptography and biometrics, several recent works proposed to build the protection in the biometric templates themselves. While these solutions can increase the confidence in biometric systems when biometric information is stored for verification, they have been shown difficult to apply to real biometrics. In this work we present a biometric authentication technique that exploits multiple biometric traits. It is privacy-aware as it ensures privacy protection and allows the extraction of secure identifiers by means of cryptographic primitives. We also discuss the implementation of our approach by considering, as a significant example, the combination of iris and fingerprint biometrics and present experimental results obtained from real data. The implementation shows the feasibility of the scheme in practical applications.

A privacy-compliant fingerprint recognition system based on homomorphic encryption and Fingercode templates

The privacy protection of the biometric data is an important research topic, especially in the case of distributed biometric systems. In this scenario, it is very important to guarantee that biometric data cannot be steeled by anyone, and that the biometric clients are unable to gather any information different from the single user verification/identification. In a biométrie system with high level of privacy compliance, also the server that processes the biométrie matching should not learn anything on the database and it should be impossible for the server to exploit the resulting matching values in order to extract any knowledge about the user presence or behavior. Within this conceptual framework, in this paper we propose a novel complete demonstrator based on a distributed biométrie system that is capable to protect the privacy of the individuals by exploiting cryptosystems. The implemented system computes the matching task in the encrypted domain by exploiting homomorphic encryption and using Fingercode templates. The paper describes the design methodology of the demonstrator and the obtained results. The demonstrator has been fully implemented and tested in real applicative conditions. Experimental results show that this method is feasible in the cases where the privacy of the data is more important than the accuracy of the system and the obtained computational time is satisfactory.

Robust Segmentation and Measurements Techniques of White Cells in Blood Microscope Images

The 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 operators 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

Fingerprint Biometrics via Low-cost Sensors and Webcams

The diffusion of mobile cameras and webcams is rapidly growing. Unfortunately, images produced by these kinds of sensors during the acquisition of human fingertips are very different from the images obtained by dedicated fingerprint sensors, especially as quality is concerned. At the present stage of the research, fingerprint biometrics can be successfully achieved in real-life applications only by using dedicated sensors and scanners. In the literature a paramount quantity of methods which are extremely effective in processing fingerprints obtained by classical sensors and procedures is presented. In this paper, we investigate new techniques to suitably process the camera images of fingertips in order to produce images which are as similar as possible to the ones coming from dedicated sensors. This will allow for directly reusing the large and valuable experience presented in the literature for fingerprint recognition and verification in environments in which mobile cameras and webcams are already or can easily become available and dedicated devices are not required by the desired security level. The proposed technique encompasses a prefiltering step, the segmentation of the fingertip image, a fingertip registration phase, the dedicated processing techniques for the ridge enhancement, and a post-processing phase. In our research we tested the identification capability of the proposed methods by using a state-of-the-art, public software for minutiae extraction and matching. The effects of different registration algorithms on the identification accuracy are also discussed and the final system has been compared with the use of commercial dedicated sensors.

Quality assessment of biometric systems: a comprehensive perspective based on accuracy and performance measurement

Despite the efforts of the international biometric community, the measurement of the accuracy of a biometric system is far from being completely investigated and, eventually, standardized. This paper presents a critical analysis of the accuracy and performance measurement of a biometric system. Current approaches to the problem and procedural error have been described and criticized. Finally, a methodology for the measurement of the accuracy of biometric systems with nonsymmetric matching function will be proposed and discussed.