A fourier-based algorithm for micro-calcification enhancement in mammographic images

Fauci, F.; Manna, A. La; Cascio, Donato; Magro, R.; Raso, G.; Iacomi, M.; Vasile, M.

doi:10.1109/nssmic.2008.4774254

Cited by 10 publications

(9 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Once the 3D shape and volume of the nodule are obtained, all seven commonly used features [30][31][32][33][34] based on shape, size, intensity can be extracted. In this study, along with the three geometrical features (surface, volume and sphericity), we also use four intensity distribution features (mean, standard deviation, skewness and kurtosis).…”

Section: Features Extractionmentioning

confidence: 99%

Automatic detection of lung nodules in CT datasets based on stable 3D mass–spring models

Cascio

Magro

Fauci

et al. 2012

Computers in Biology and Medicine

Self Cite

127

View full text Add to dashboard Cite

a b s t r a c tWe 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 radiodensity 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 developed an advanced computerized method for the automatic detection of internal and juxtapleural nodules on low-dose and thin-slice lung CT scan. This method consists of an initial selection of nodule candidates list, the segmentation of each candidate nodule and the classification of the features computed for each segmented nodule candidate.The presented CAD system is aimed to reduce the number of omissions and to decrease the radiologist scan examination time. Our system locates with the same scheme both internal and juxtapleural nodules. For a correct volume segmentation of the lung parenchyma, the system uses a Region Growing (RG) algorithm and an opening process for including the juxtapleural nodules. The segmentation and the extraction of the suspected nodular lesions from CT images by a lung CAD system constitutes a hard task. In order to solve this key problem, we use a new Stable 3D Mass-Spring Model (MSM) combined with a spline curves reconstruction process. Our model represents concurrently the characteristic gray value range, the directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. For distinguishing the real nodules among nodule candidates, an additional classification step is applied; furthermore, a neural network is applied to reduce the false positives (FPs) after a doublethreshold cut. The system performance was tested on a set of 84 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. The detection rate of the system is 97% with 6.1 FPs/CT. A reduction to 2.5 FPs/CT is achieved at 88% sensitivity. We presented a new 3D segmentation technique for lung nodules in CT datasets, using deformable MSMs. The result is a efficient segmentation process able to converge, identifying the shape of the generic ROI, after a few iterations. Our suitable results show that the use of the 3D AC model and the feature analysis based FPs reduction process constitutes an accurate approach to the segmentation and the classification of lung nodules.

show abstract

Section: Features Extractionmentioning

confidence: 99%

Automatic detection of lung nodules in CT datasets based on stable 3D mass–spring models

Cascio

Magro

Fauci

et al. 2012

Computers in Biology and Medicine

Self Cite

127

View full text Add to dashboard Cite

show abstract

“…patterns, it appears clearly that e mainly based based mainly on on of bright/dark structures: y and colors. It seems natural to analysis of texture and more eal with bright/dark speckle-like an be characterized by a limited he spatial relationships between age variations occurring in each generally obtained by means of hese techniques can be grouped ) statistical methods describing in the image; and (ii) frequency ge variations [5,6]. To achieve the objective of robust classification, we combine several discriminative visual features known to be effective for cell classifications with a robust and scalable multi-class boosting.…”

Section: Methodsmentioning

confidence: 99%

HEp-2 Cell Classification with Heterogeneous Classes-Processes Based on K-Nearest Neighbours

Cascio

Taormina

Cipolla

et al. 2014

2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images

View full text Add to dashboard Cite

We present a scheme for the feature extraction and classification of the fluorescence staining patterns of HEp-2 cells in IIF images. We propose a set of complementary processes specific to each class of patterns to search. Our set of processes consists of preprocessing, features extraction and classification. The choice of methods, features and parameters was performed automatically, using the Mean Class Accuracy (MCA) as a figure of merit. We extract a large number (108) of features able to fully characterize the staining pattern of HEp-2 cells. We propose a classification approach based on two steps: the first step follows the one-against-all (OAA) scheme, while the second step follows the oneagainst-one (OAO) scheme. To do this, we needed to implement 21 KNN classifiers: 6 OAA and 15 OAO. Leave-one-out image cross validation method was used for the evaluation of the results.

show abstract

“…CNNs eliminate the need for manual feature extraction [17][18][19], so the user does not have to identify features used for image classification. In fact, it is possible to use the power of the pre-trained networks, without investing time and effort in training, to implement the extraction phase of the characteristics.…”

Section: Deep Cnnmentioning

confidence: 99%

Deep Convolutional Neural Network for HEp-2 Fluorescence Intensity Classification

2019

View full text Add to dashboard Cite

Indirect ImmunoFluorescence (IIF) assays are recommended as the gold standard method for detection of antinuclear antibodies (ANAs), which are of considerable importance in the diagnosis of autoimmune diseases. Fluorescence intensity analysis is very often complex, and depending on the capabilities of the operator, the association with incorrect classes is statistically easy. In this paper, we present a Convolutional Neural Network (CNN) system to classify positive/negative fluorescence intensity of HEp-2 IIF images, which is important for autoimmune diseases diagnosis. The method uses the best known pre-trained CNNs to extract features and a support vector machine (SVM) classifier for the final association to the positive or negative classes. This system has been developed and the classifier was trained on a database implemented by the AIDA (AutoImmunité, Diagnostic Assisté par ordinateur) project. The method proposed here has been tested on a public part of the same database, consisting of 2080 IIF images. The performance analysis showed an accuracy of fluorescent intensity around 93%. The results have been evaluated by comparing them with some of the most representative state-of-the-art works, demonstrating the quality of the system in the intensity classification of HEp-2 images.

show abstract

A fourier-based algorithm for micro-calcification enhancement in mammographic images

Cited by 10 publications

References 3 publications

Automatic detection of lung nodules in CT datasets based on stable 3D mass–spring models

Automatic detection of lung nodules in CT datasets based on stable 3D mass–spring models

HEp-2 Cell Classification with Heterogeneous Classes-Processes Based on K-Nearest Neighbours

Deep Convolutional Neural Network for HEp-2 Fluorescence Intensity Classification

Contact Info

Product

Resources

About