0000000000179064
AUTHOR
M. Iacomi
Mammographic images segmentation based on chaotic map clustering algorithm
Background: This work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images. The chaotic map clustering algorithm is used to group together similar subsets of image pixels resulting in a medically meaningful partition of the mammography. Methods: The image is divided into pixels subsets characterized by a set of conveniently chosen features and each of the corresponding points in the feature space is associated to a map. A mutual coupling strength between the maps depending on the associated distance between feature space points is subsequently introduced. On the system of maps, the simulated evolution through chaotic dynamics leads…
Moment equations in a Lotka-Volterra extended system with time correlated noise
A spatially extended Lotka-Volterra system of two competing species in the presence of two correlated noise sources is analyzed: (i) an external multiplicative time correlated noise, which mimics the interaction between the system and the environment; (ii) a dichotomous stochastic process, whose jump rate is a periodic function, which represents the interaction parameter between the species. The moment equations for the species densities are derived in Gaussian approximation, using a mean field approach. Within this formalism we study the effect of the external time correlated noise on the ecosystem dynamics. We find that the time behavior of the $1^{st}$ order moments are independent on th…
Superior Performances of the Neural Network on the Masses Lesions Classification through Morphological Lesion Differences
Purpose of this work is to develop an automatic classification system that could be useful for radiologists in the breast cancer investigation. The software has been designed in the framework of the MAGIC-5 collaboration. In an automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based generally on morphological lesion differences. A study in the space features representation is made and some classifiers are tested to distinguish the pathological regions from the healthy ones. The results provided in terms of sensitivity and specificity will be p…
A fourier-based algorithm for micro-calcification enhancement in mammographic images
Breast cancer is the most widespread cancer in women in the world; it manifests mostly in two forms: microcalcifications and massive lesions. These two forms differ in density, size, shape and number. Consequently, there are two different kinds of mammographic CAD algorithms: those for microcalcifications detection, and those for massive lesions detection. The microcalcifications detection is a hard task, since they are quite small and often poorly contrasted against the background, especially in images affected by digitization noise. In a CAD system the ROI Hunter plays an important role, because missed microcalcifications at this level are definitely lost. For this reason, highlighting me…
A method to reduce the FP/imm number through CC and MLO views comparison in mammographic images
In this paper we propose a method to reduce the FP/imm number through CC and MLO mammographic views comparison of the same patient. The proposed solution uses the symmetry properties of the breast to compute a geometric transformation that permits to represent the two images in comparable coordinates systems. Through this method, potential pathological ROIs of one of the projections are correlated with the ROIs in the second view. To show the effectiveness of the result we apply the method on a dataset composed of 112 couples of pathological images. Experiments shows that method enables a reduction by up to 700/0 of the FP/imm number detected after the classification step
Computer-aided diagnosis in digital mammography: comparison of two commercial systems
Aim: Within this work, a comparative analysis of two commercial computer-aided detection or diagnosis (CAD) systems, CyclopusCAD® mammo (v. 6.0) produced by CyclopusCAD Ltd (Palermo, Italy) and SecondLook® (v. 6.1C) produced by iCAD Inc. (OH, USA) is performed by evaluating the results of both systems application on an unique set of mammographic digital images routinely acquired in a hospital structure. Materials & methods: The two CAD systems have been separately applied on a sample set of 126 mammographic digital cases, having been independently diagnosed by two senior radiologists. According to the human diagnosis, the cases in the sample reference set are divided into 61 negatives and 6…
Automatic detection of lung nodules in CT datasets based on stable 3D mass–spring models
We propose a computer-aided detection (CAD) system which can detect small-sized (from 3 mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung cancer and their early stage detection arguably improves the patient survival rate. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the full analysis difficult, leading to omission of nodules by the radiologist. We…