Federica Caselli

Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing

Mammography is the most effective method for the early detection of breast diseases. However, the typical diagnostic signs such as microcalcifications and masses are difficult to detect because mammograms are low-contrast and noisy images. In this paper, a novel algorithm for image denoising and enhancement based on dyadic wavelet processing is proposed. The denoising phase is based on a local iterative noise variance estimation. Moreover, in the case of microcalcifications, we propose an adaptive tuning of enhancement degree at different wavelet scales, whereas in the case of mass detection, we developed a new segmentation method combining dyadic wavelet information with mathematical morphology. The innovative approach consists of using the same algorithmic core for processing images to detect both microcalcifications and masses. The proposed algorithm has been tested on a large number of clinical images, comparing the results with those obtained by several other algorithms proposed in the literature through both analytical indexes and the opinions of radiologists. Through preliminary tests, the method seems to meaningfully improve the diagnosis in the early breast cancer detection with respect to other approaches.

An impedance-based flow microcytometer for single cell morphology discrimination.

Cell shape is a fundamental biological feature, providing specific information about physiological or pathological cellular conditions. Most of the state-of-the-art microfluidic cytometers, however, only allow simple cell analysis, including viability studies, cell counting and sorting. In this work, we present a non-invasive, label-free device capable of single cell morphology discrimination in continuous flow. The device is based on the principle of liquid electrodes, fabricated in a cross configuration around a sensing zone. This arrangement allows measurement of cell impedance along orthogonal orientations and extraction of an index describing cell shape anisotropy. By adding prior to the sensing volume a series of lateral liquid electrodes, the particle stream was focused toward the channel midline and each cell was oriented in a specific direction before shape sensing. We demonstrate the proof of concept by performing spherical and elongated particle discrimination. As an application, we show that the shape changes experienced during cell division can be monitored and characterized. In particular, budding yeasts at different stages of the mitotic cycle were identified by extracting their anisotropy index.

EIT-Inspired Microfluidic Cytometer for Single-Cell Dielectric Spectroscopy

A new microfluidic cytometer for single-cell dielectric spectroscopy is proposed in this paper and analyzed in silico by means of a finite-element model. The device, inspired by electrical impedance tomography, includes two circumferential arrays of electrodes instead of just two pairs of coplanar or parallel-facing electrodes, thus allowing a great versatility in stimulation and measurement patterns. In particular, using stimulation patterns with different spatial orientation provides information on cell morphology, besides quantitative cell-volume estimation. Moreover, the performance limitation at low frequency due to electrode polarization is overcome, owing to a peculiar recording scheme: Current is injected between an electrode pair, and the resulting voltages are measured at remaining electrodes using high-input impedance differential amplifiers. These features significantly enhance the cytometer discrimination capabilities.

A Simple and Robust Event-Detection Algorithm for Single-Cell Impedance Cytometry

Microfluidic impedance cytometry is emerging as a powerful label-free technique for the characterization of single biological cells. In order to increase the sensitivity and the specificity of the technique, suited digital signal processing methods are required to extract meaningful information from measured impedance data. In this study, a simple and robust event-detection algorithm for impedance cytometry is presented. Since a differential measuring scheme is generally adopted, the signal recorded when a cell passes through the sensing region of the device exhibits a typical odd-symmetric pattern. This feature is exploited twice by the proposed algorithm: first, a preliminary segmentation, based on the correlation of the data stream with the simplest odd-symmetric template, is performed; then, the quality of detected events is established by evaluating their E2 O index, that is, a measure of the ratio between their even and odd parts. A thorough performance analysis is reported, showing the robustness of the algorithm with respect to parameter choice and noise level. In terms of sensitivity and positive predictive value, an overall performance of 94.9% and 98.5%, respectively, was achieved on two datasets relevant to microfluidic chips with very different characteristics, considering three noise levels. The present algorithm can foster the role of impedance cytometry in single-cell analysis, which is the new frontier in “Omics”.

Performance evaluation of a region growing procedure for mammographic breast lesion identification

At present, mammography is the most effective examination for an early diagnosis of breast cancer. Nevertheless, the detection of cancer signs in mammograms is a difficult procedure owing to the great number of non-pathological structures which are also present in the image. Recent statistics show that in current breast cancer screenings 10%-25% of the tumors are missed by the radiologists. For this reason, a lot of research is currently being done to develop systems for Computer Aided Detection (CADe). Probably, some causes of the false-negative screening examinations are that tumoral masses have varying dimension and irregular shape, their borders are often ill-defined and their contrast is very low, thus making difficult the discrimination from parenchymal structures. Therefore, in a CADe system a preliminary segmentation procedure has to be implemented in order to separate the mass from the background tissue. In this way, various characteristics of the segmented mass can be evaluated and used in a classification step to discriminate benign and malignant cases. In this paper, we describe an effective algorithm for massive lesions segmentation based on a region-growing technique and we provide full details the performance evaluation procedure used in this specific context.

A simple formula for the effective complex conductivity of periodic fibrous composites with interfacial impedance and applications to biological tissues

This paper presents a simple analytical expression for the effective complex conductivity of a periodic hexagonal arrangement of conductive circular cylinders embedded in a conductive matrix, with interfaces exhibiting a capacitive impedance. This composite material may be regarded as an idealized model of a biological tissue comprising tubular cells, such as skeletal muscle. The asymptotic homogenization method is adopted, and the corresponding local problem is solved by resorting to Weierstrass elliptic functions. The effectiveness of the present analytical result is proved by convergence analysis and comparison with finite-element solutions and existing models.

An innovative iterative thresholding algorithm for tumour segmentation and volumetric quantification on SPECT images: Monte Carlo-based methodology and validation

PURPOSE Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging play an important role in the segmentation of functioning parts of organs or tumours, but an accurate and reproducible delineation is still a challenging task. In this work, an innovative iterative thresholding method for tumour segmentation has been proposed and implemented for a SPECT system. This method, which is based on experimental threshold-volume calibrations, implements also the recovery coefficients (RC) of the imaging system, so it has been called recovering iterative thresholding method (RIThM). The possibility to employ Monte Carlo (MC) simulations for system calibration was also investigated. METHODS The RIThM is an iterative algorithm coded using MATLAB: after an initial rough estimate of the volume of interest, the following calculations are repeated: (i) the corresponding source-to-background ratio (SBR) is measured and corrected by means of the RC curve; (ii) the threshold corresponding to the amended SBR value and the volume estimate is then found using threshold-volume data; (iii) new volume estimate is obtained by image thresholding. The process goes on until convergence. The RIThM was implemented for an Infinia Hawkeye 4 (GE Healthcare) SPECT/CT system, using a Jaszczak phantom and several test objects. Two MC codes were tested to simulate the calibration images: SIMIND and SimSet. For validation, test images consisting of hot spheres and some anatomical structures of the Zubal head phantom were simulated with SIMIND code. Additional test objects (flasks and vials) were also imaged experimentally. Finally, the RIThM was applied to evaluate three cases of brain metastases and two cases of high grade gliomas. RESULTS Comparing experimental thresholds and those obtained by MC simulations, a maximum difference of about 4% was found, within the errors (+/- 2% and +/- 5%, for volumes > or = 5 ml or < 5 ml, respectively). Also for the RC data, the comparison showed differences (up to 8%) within the assigned error (+/- 6%). ANOVA test demonstrated that the calibration results (in terms of thresholds or RCs at various volumes) obtained by MC simulations were indistinguishable from those obtained experimentally. The accuracy in volume determination for the simulated hot spheres was between -9% and 15% in the range 4-270 ml, whereas for volumes less than 4 ml (in the range 1-3 ml) the difference increased abruptly reaching values greater than 100%. For the Zubal head phantom, errors ranged between 9% and 18%. For the experimental test images, the accuracy level was within +/- 10%, for volumes in the range 20-110 ml. The preliminary test of application on patients evidenced the suitability of the method in a clinical setting. CONCLUSIONS The MC-guided delineation of tumor volume may reduce the acquisition time required for the experimental calibration. Analysis of images of several simulated and experimental test objects, Zubal head phantom and clinical cases demonstrated the robustness, suitability, accuracy, and speed of the proposed method. Nevertheless, studies concerning tumors of irregular shape and/or nonuniform distribution of the background activity are still in progress.

Modeling, Simulation, and Performance Evaluation of a Novel Microfluidic Impedance Cytometer for Morphology-Based Cell Discrimination

The performance of a novel microfluidic impedance cytometer [1] for single-cell analysis is investigated in-silico by means of a finite element model. The main feature of the device is the ability to probe impedance of flowing cells along two orthogonal directions. As proved by means of numerical simulations involving spherical and ellipsoidal cells, this allows to extract information on cell morphology. In particular, simple anisotropy indices are devised, which are independent from cell volume and rather insensitive to small imperfections in the focusing system. In addition, simulations with budding yeasts show the capability of the device to identify the cell division stage.

Wavelet based adaptive algorithm for mammographic images enhancement and denoising

Mammography is the most effective method for early detection of breast diseases. However, the typical diagnostic signs, such as masses and microcalcifications, are difficult to be detected because mammograms are low contrast and noisy images. In this paper, we present an algorithm for mammographic images enhancement and denoising based on the wavelet transform. In particular, we develop an adaptive procedure to perform an optimal denoising using a local iterative fuzzy noise variance estimation. Moreover, the degree of enhancement is adaptively tuned at each scale. The proposed algorithm has been tested on phantom and clinical images.

Mammographic Images Enhancement and Denoising for Microcalbfication Detection Using Dyadic Wavelet Processing

Mammography is the most effective method for early detection of breast diseases. However, the typical diagnostic signs, such as masses and microcalcifications, are difficult to be detected because mammograms are low contrast and noisy images. In this paper, we present an algorithm for mammographic images enhancement and denoising based on the wavelet transform. In particular, we develop an adaptive procedure to perform an optimal denoising using a local iterative fuzzy noise variance estimation. Moreover, the degree of enhancement is adoptively tuned at each scale. The proposed algorithm has been tested on clinical images