R. Massafra

Detection of subclinical brain electrical activity changes in Huntington's disease using artificial neural networks

OBJECTIVE The aim of this study was to analyze EEG background activity in Huntingtons disease (HD) patients and relatives at risk, in relation to CAG repeat size and clinical state, in order to detect an electrophysiological marker of early disease. METHODS We selected 13 patients and 7 subjects at risk. Thirteen normal subjects, sex- and age-matched, were also evaluated. Artifact-free epochs were selected and analyzed through Fast-Fourier Transform. EEG background activity was tested using both linear analysis and artificial neural network (ANN) classifier in order to evaluate whether EEG abnormalities were linked to functional changes preceding the onset of the disease. RESULTS The most important EEG classification pattern was the absolute alpha power not correlated with cognitive decline. The ANN correctly classified 11/13 patients and 12/13 normals. Moreover, the neural scores for subjects at risk seemed to be correlated to the expected time before the onset of the disease. CONCLUSIONS ANN is a very powerful method to discriminate between normals and patients. It could be used as an automatic diagnostic tool. EEG changes in positive gene-carriers for HD confirm an early functional impairment which should be taken into account in the genetic counseling and in the management of the early stages of the disease.

MAGIC-5: an Italian mammographic database of digitised images for research

The implementation of a database of digitised mammograms is discussed. The digitised images were collected beginning in 1999 by a community of physicists in collaboration with radiologists in several Italian hospitals as a first step in developing and implementing a computer-aided detection (CAD) system. All 3,369 mammograms were collected from 967 patients and classified according to lesion type and morphology, breast tissue and pathology type. A dedicated graphical user interface was developed to visualise and process mammograms to support the medical diagnosis directly on a high-resolution screen. The database has been the starting point for developing other medical imaging applications, such as a breast CAD, currently being upgraded and optimised for use in a distributed environment with grid services, in the framework of the Instituto Nazionale di Fisicia Nucleare (INFN)-funded Medical Applications on a Grid Infrastructure Connection (MAGIC)-5 project.RiassuntoIn qesto lavoro viene discussa l’implementazione di un database immagini mammografiche digitalizzate. Le immagini sono state raccolte dal 1999 da un gruppo di fisici in collaborazione con radiology di alcuni ospedali italiani, come primo passo dello sviluppo e implementazione di un sistema di Computer Aided Detection (CAD). I 3369 mammogrammi appartengono a 967 pazienti e sono classificati secondo I tipi e la morfologia delle lesioni, il tessuto mammario e i tipi di patologie. Una interfaccia grafica opportunamente progettata è stata sviluppata per la visualizzazione e l’elaborazione delle mammografie digitalizzate al fine di runpoter supportare direttamente una diagnosi medica su monitor ad alta risoluzione. Il database ha rappresentato il punto di partenza per lo sviluppo di altre applicazioni di imaging medicale come il CAD mammografico costantemente ottimizzato e aggiornato con l’uso di un ambiente distribuito che dispone di servizi GRID, nel framework del progetto MAGIC-5, finanziato dell’INFN.

GPCALMA: an italian mammographic database of digitized images for research

In this work the implementation of a database of digitized mammograms is described. The digitized images were collected since 1999 by a community of physicists in collaboration with radiologists in several Italian hospitals, as a first step in order to develop and implement a Computer Aided Detection (CAD) system. 3369 mammograms were collected from 967 patients; they were classified according to the type and the morphology of the lesions, the type of the breast tissue and the type of pathologies. A dedicated Graphical User Interface was developed for mammography visualization and processing, in order to support the medical diagnosis directly on a high-resolution screen. The database has been the starting point for the development of other medical imaging applications such as a breast CAD, currently being upgraded and optimized for the use in conjunction of the GRID technology in the framework of the INFN-funded MAGIC-5 project.

A Combined Approach of Multiscale Texture Analysis and Interest Point/Corner Detectors for Microcalcifications Diagnosis

Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic model for characterizing and discriminating tissue in normal/abnormal and benign/malign in digital mammograms, as support tool for the radiologists. We trained a Random Forest classifier on some textural features extracted on a multiscale image decomposition based on the Haar wavelet transform combined with the interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg), respectively. We tested the proposed model on 192 ROIs extracted from 176 digital mammograms of a public database. The model proposed was high performing in the prediction of the normal/abnormal and benign/malignant ROIs, with a median AUC value of \(98.46\%\) and \(94.19\%\), respectively. The experimental result was comparable with related work performance.

A Gradient-Based Approach for Breast DCE-MRI Analysis

Breast cancer is the main cause of female malignancy worldwide. Effective early detection by imaging studies remains critical to decrease mortality rates, particularly in women at high risk for developing breast cancer. Breast Magnetic Resonance Imaging (MRI) is a common diagnostic tool in the management of breast diseases, especially for high-risk women. However, during this examination, both normal and abnormal breast tissues enhance after contrast material administration. Specifically, the normal breast tissue enhancement is known as background parenchymal enhancement: it may represent breast activity and depends on several factors, varying in degree and distribution in different patients as well as in the same patient over time. While a light degree of normal breast tissue enhancement generally causes no interpretative difficulties, a higher degree may cause difficulty to detect and classify breast lesions at Magnetic Resonance Imaging even for experienced radiologists. In this work, we intend to investigate the exploitation of some statistical measurements to automatically characterize the enhancement trend of the whole breast area in both normal and abnormal tissues independently from the presence of a background parenchymal enhancement thus to provide a diagnostic support tool for radiologists in the MRI analysis.

Hough transform for clustered microcalcifications detection in full-field digital mammograms

Many screening programs use mammography as principal diagnostic tool for detecting breast cancer at a very early stage. Despite the efficacy of the mammograms in highlighting breast diseases, the detection of some lesions is still doubtless for radiologists. In particular, the extremely minute and elongated salt-like particles of microcalcifications are sometimes no larger than 0.1 mm and represent approximately half of all cancer detected by means of mammograms. Hence the need for automatic tools able to support radiologists in their work. Here, we propose a computer assisted diagnostic tool to support radiologists in identifying microcalcifications in full (native) digital mammographic images. The proposed CAD system consists of a pre-processing step, that improves contrast and reduces noise by applying Sobel edge detection algorithm and Gaussian filter, followed by a microcalcification detection step performed by exploiting the circular Hough transform. The procedure performance was tested on 200 images coming from the Breast Cancer Digital Repository (BCDR), a publicly available database. The automatically detected clusters of microcalcifications were evaluated by skilled radiologists which asses the validity of the correctly identified regions of interest as well as the system error in case of missed clustered microcalcifications. The system performance was evaluated in terms of Sensitivity and False Positives per images (FPi) rate resulting comparable to the state-of-art approaches. The proposed model was able to accurately predict the microcalcification clusters obtaining performances (sensibility = 91.78% and FPi rate = 3.99) which favorably compare to other state-of-the-art approaches.