Mammogram segmentation by contour searching and massive lesion classification with neural network

Fauci,; Bagnasco,; Bellotti, Vittorio; Cascio,; Cheran,; Sc, Stemina; Asmundis, Carlo de; De, Nunzio; Fantacci, R.; Me, M.E.; Forni,; Lauria, Mario; Торрес,; El, E.L.; Magro, Gaetano; Masala,; Gl, G.L.; Oliva, Piernicola; Quarta, Giulio; Raso,; Retico, Alessandra; Tangaro,

doi:10.1109/nssmic.2004.1462823

Cited by 20 publications

(15 citation statements)

References 9 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

“…Some other approaches are based on the edge detection of mammogram components [5,10,21,25,28,34,37] We also find, in this category, clustering based approaches. They consist of detecting clusters which may represent an eventual tumor [12,48].…”

Section: ) Single View Lesions Detectionmentioning

confidence: 70%

“…For example, region-based approaches depend on the seed selection and the algorithm ending conditions. Some techniques (mainly fractal model technique) are known as time-consuming [28].…”

Section: ) Single View Lesions Detectionmentioning

confidence: 99%

A New CAD System for Breast Microcalcifications Diagnosis

Boulehmi¹,

Mahersia²,

Hamrouni³

2016

ijacsa

View full text Add to dashboard Cite

In this paper a new approach of breast microcalcifications diagnosis on digital mammograms is introduced. The proposed approach begins with a preprocessing procedure aiming artifacts and pectoral muscle removal based on morphologic operators and contrast enhancement based on galactophorous tree interpolation.The second step of the proposed CAD system consists on segmenting microcalcifications clusters, using Generalized Gaussian Density (GGD) estimation and a Bayesian backpropagation neural network.The last step is microcalcifications characterization using morphologic features which are used to feed a neuro-fuzzy system to classify the detected breast microcalcifications into benign and malignant classes.

show abstract

“…Fauci et al [11] developed an edge-based segmentation algorithm that uses iterative procedure, a ROI Hunter algorithm for selecting ROIs. ROI Hunter algorithm is based on the search of relative intensity maximum inside the square windows that form the mammographic image.…”

Section: Mammogram Segmentation Methodsmentioning

confidence: 99%

Survey on Segmentation Methods for Locating Masses in a Mammogram Image

Bandyopadhyay¹

2010

IJCA

View full text Add to dashboard Cite

A digital mammogram generally detects varying degrees of breast cancer such as clustered microcalcifications, speculated lesions, circumscribed masses, ill-defined masses, and architectural distortions. Many methods of analysing digital mammograms have been recently examined and yielded varied success.Common techniques from the field of image processing have been applied to digital mammograms in an effort to locate signs of cancer sooner and more precisely than previously possible. Research suggests that computerized techniques applied/utilized by radiologists will be highly successful in analysing digital mammograms. Computerized systems that draw attention to areas of suspicion, otherwise less noticeable to radiologists have the potential to greatly increase early detection.Previously, Algorithms that effectively segment mammogram images into major sub-components and also meets the goals of efficiency and generality has been lacking. This paper reviews some methods of mammogram segmentation process for detection of masses in breast.

show abstract

Mammogram segmentation by contour searching and massive lesion classification with neural network

Cited by 20 publications

References 9 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

A New CAD System for Breast Microcalcifications Diagnosis

Survey on Segmentation Methods for Locating Masses in a Mammogram Image

Contact Info

Product

Resources

About