Enhancement of microcalcifications in digitized mammograms: Multifractal and mathematical morphology approach

Prikazana su dva metoda isticanja mikrokalcifikacija u digitalnim mamogramima. Prvi metod zasnovan je na multifraktalnoj analizi digitalne slike, a drugi na primeni moderne matematičke morfologije. U multifraktalnom pristupu kreiraju se multifraktalne 'slike' izvornog mamograma, na osnovu kojih se dalje interaktivno bira nivo segmentacije detalja. Drugi metod, pogodnom kombinacijom morfoloških operacija, povećava lokalni kontrast uz snažno potiskivanje pozadinske teksture, nezavisno od radiološke gustine tkiva dojke. Iterativnim postupkom morfološki metod visoko ističe samo male detalje sjajnije od okolnog tkiva, potencijalne mikrokalcifikacije. Interaktivni pristup kod oba metoda omogućava radiologu da kontroliše nivo izdvajanja detalja. Predloženi metodi su testirani na referentnim mamogramima iz miniMIAS baze i iz kliničke prakse.Two methods for enhancing the microcalcifications in digitized mammograms are under consideration. First method is based on multifractal approach, and second on modern mathematical morphology. In multifractal approach, from initial mammogram image, a corresponding multifractal 'images' are created, from which a radiologist has a freedom to change the level of segmentation in an interactive manner. The second method, using an appropriate combination of some morphological operations, enables high local contrast enhancement, followed by significant suppression of background tissue, irrespective of the radiology density of the tissue. By iterative procedure this method highly emphasizes only small bright details, possible microcalcifications. The interactive approach enables the physician to control the level of segmentation. Suggested methods were tested through referent mammograms from MiniMIAS database and from clinical praxis mammograms

Computer aided system for segmentation and visualization of microcalcifications in digital mammograms.

Two methods for segmentation and visualization of microcalcifications in digital or digitized mammograms are described. First method is based on modern mathematical morphology, while the second one uses the multifractal approach. In the first method, by using an appropriate combination of some morphological operations, high local contrast enhancement, followed by significant suppression of background tissue, irrespective of its radiology density, is obtained. By iterative procedure, this method highly emphasizes only small bright details, possible microcalcifications. In a multifractal approach, from initial mammogram image, a corresponding multifractal "images" are created, from which a radiologist has a freedom to change the level of segmentation. An appropriate user friendly computer aided visualization (CAV) system with embedded two methods is realized. The interactive approach enables the physician to control the level and the quality of segmentation. Suggested methods were tested through mammograms from MIAS database as a gold standard, and from clinical praxis, using digitized films and digital images from full field digital mammograph

Computer aided system for segmentation and visualization of microcalcifications in digital mammograms

Two methods for segmentation and visualization of microcalcifications in digital or digitized mammograms are described. First method is based on modern mathematical morphology, while the second one uses the multifractal approach. In the first method, by using an appropriate combination of some morphological operations, high local contrast enhancement, followed by significant suppression of background tissue, irrespective of its radiology density, is obtained. By iterative procedure, this method highly emphasizes only small bright details, possible microcalcifications. In a multifractal approach, from initial mammogram image, a corresponding multifractal "images" are created, from which a radiologist has a freedom to change the level of segmentation. An appropriate user friendly computer aided visualization (CAV) system with embedded two methods is realized. The interactive approach enables the physician to control the level and the quality of segmentation. Suggested methods were tested through mammograms from MIAS database as a gold standard, and from clinical praxis, using digitized films and digital images from full field digital mammograph

Detection of microcalcifications in mammograms using error of prediction and statistical measures

A two-stage method for detecting microcalcifications in mammograms is presented. In the first stage, the determination of the candidates for microcalcifications is performed. For this purpose, a 2-D linear prediction error filter is applied, and for those pixels where the prediction error is larger than a threshold, a statistical measure is calculated to determine whether they are candidates for microcalcifications or not. In the second stage, a feature vector is derived for each candidate, and after a classification step using a support vector machine, the final detection is performed. The algorithm is tested with 40 mammographic images, from Screen Test: The Alberta Program for the Early Detection of Breast Cancer with 50- m resolution, and the results are evaluated using a freeresponse receiver operating characteristics curve. Two different analyses are performed: an individual microcalcification detection analysis and a cluster analysis. In the analysis of individual microcalcifications, detection sensitivity values of 0.75 and 0.81 are obtained at 2.6 and 6.2 false positives per image, on the average, respectively. The best performance is characterized by a sensitivity of 0.89, a specificity of 0.99, and a positive predictive value of 0.79. In cluster analysis, a sensitivity value of 0.97 is obtained at 1.77 false positives per image, and a value of 0.90 is achieved at 0.94 false positive per imag

Performance of a CAD scheme applied to images obtained from mammographic film digitization and full-field digital mammography (FFDM).

This work has as purpose to compare the effects of a CAD scheme applied to digitized and \ud direct digital mamograms sets. A routine designed to be applied to mammogram in \ud DICOM standard was developed and a schema based on the Watershed Transform to \ud masses detection was applied to 252 ROIs from 130 digitized mammograms, resulting in \ud 92% of true positive and 10% of false positives. For clustered microcalcifications \ud detection, another procedure was applied to 165 ROIs from 120 mammograms, resulting in \ud 93% of true positive and 16% of false positive. By using the same procedures to 154 \ud digital mammograms obtained from FFDM, the rates have shown a little decrease in the \ud scheme performance: 89% of true positive and 16% of false positive for masses detection; \ud 90% of true positive and 27% of false positive for clusters detection. Although the tests \ud with digital mammograms have been carried with a smaller number of images and \ud different cases compared to the digitized ones, including several dense breasts images, the \ud results can be considered comparable, mainly forclustered microcalcifications detection \ud with a difference of only 3% between the sensibility rates for the both images sets. Another \ud important feature affecting these results is the contrast difference between the two images \ud set. This implies the need of extensive investigations not only with a larger number of \ud cases from FFDM but also on the parameters related to its image acquisition as well as to \ud its corresponding processing.Este trabalho tem como objetivo comparar os resultados de um esquema CAD aplicado em \ud conjunto de mamografias digitalizadas e em um conjunto de mamografias obtidas de um \ud mamógrafo digital. Para extrair as imagens do padrão DICOM, padrão utilizado pelos \ud mamógrafos digitais, uma rotina computacional foi desenvolvida. Para a detecção de \ud nódulos, um esquema baseado em Transforma Watershed foi aplicado a 252 regiões de \ud interesse (ROIs) de 130 mamografias digitalizadas, resultando em 92% de verdadeiro \ud positivo e 10%de falsos positivos. Para a detecção de microcalcificações agrupadas, outro \ud procedimento foi aplicado a165 ROIs extraídas de 120 mamografias digitalizadas, \ud resultando em 93% de verdadeiro positivo e 16% de falso positivo. Ao utilizar os mesmos \ud procedimentos para154 mamografias digitais obtidas a partir de um FFDM, as taxas \ud mostraram uma diminuição pequena no desempenho: 89% do verdadeiro positivo e 16% \ud de falso positivo para a detecção de nódulos, e 90% de verdadeiro positivo e 27% de falsos \ud positivo para a detecção de clusters de microcalcificações. Embora os testes com \ud mamografias digitais tenham sido realizados com um menor número de imagens e casos \ud diferentes em comparação com os digitalizados, incluindo várias imagens de mamas \ud densas, os resultados podem ser considerados comparáveis, principalmente para a detecção \ud de clusters de microcalcificações com uma diferença de apenas 3% entre as taxas de \ud sensibilidade para as imagens dos dois conjuntos. Outra característica importante que afeta \ud esses resultados é a diferença de contraste dos dois grupos de imagens analisados. Isto \ud implica na necessidade de extensas investigações não só com um maior número de casos \ud de mamografias digitais, mas também um estudo sobre os parâmetros relacionados a \ud aquisição da imagem, bem como para o seu processamentoCNPqFAPESPHospital of Clinics in Botucatu/S

A novel shape feature to classify microcalcifications

Clinical evident shows that the shape of mammographic calcification is an indicator of the pathology. Microcalcifications (MC) with rough shape are early signs of malignant breast cancer. This thesis proposed a shape metric to help radiologist in classifying regions of interest. Region growing and gradient vector flow algorithm are used to obtain the contour of MC to calculate the normalized distance signature. A three level wavelet decomposition with a Daubechies eight tap wavelet is used to provide a bandpass function and extract the desired shape feature of the MC. A comparison with previously used shape features such as compactness, moment, Fourier descriptors is provided. 58 malignant and 125 benign cases, totaling 368 individual MC, are tested by the proposed method and previously used shape features

Microcalcifications Detection Using Image And Signal Processing Techniques For Early Detection Of Breast Cancer

Breast cancer has transformed into a severe health problem around the world. Early diagnosis is an important factor to survive this disease. The earliest detection signs of potential breast cancer that is distinguishable by current screening techniques are the presence of microcalcifications (MCs). MCs are small crystals of calcium apatite and their normal size ranges from 0.1mm to 0.5mm single crystals to groups up to a few centimeters in diameter. They are the first indication of breast cancer in more than 40% of all breast cancer cases, making their diagnosis critical. This dissertation proposes several segmentation techniques for detecting and isolating point microcalcifications: Otsu’s Method, Balanced Histogram Thresholding, Iterative Method, Maximum Entropy, Moment Preserving, and Genetic Algorithm. These methods were applied to medical images to detect microcalcifications. In this dissertation, results from the application of these techniques are presented and their efficiency for early detection of breast cancer is explained. This dissertation also explains theories and algorithms related to these techniques that can be used for breast cancer detection