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

Review of Different Methods of Abnormal Mass Detection in Digital Mammograms

Various images from massive image databases extract inherent, implanted information or different examples explicitly found in the images. These images may help the community in initial self-screening breast cancer, and primary health care can introduce this method to the community. This study aimed to review the different methods of abnormal mass detection in digital mammograms. One of best methods for the detection of breast malignancy and discovery at a nascent stage is digital mammography. Some of the mammograms with excellent images have a high intensity of resolution that enables preparing images with high computations. The fact that medical images are so common on computers is one of the main things that helps radiologists make diagnoses. Image preprocessing highlights the portion after extraction and arrangement in computerized mammograms. Moreover, the future scope of examination for paving could be the way for a top invention in computer-aided diagnosis (CAD) for mammograms in the coming years. This also distinguished CAD that helped identify strategies for mass widely covered in the study work. However, the identification methods for structural deviation in mammograms are complicated in real-life scenarios. These methods will benefit the public health program if they can be introduced to primary health care's public health screening system. The decision should be made as to which type of technology fits the level of the primary health care system

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

Detecting microcalcification clusters in digital mammograms: Study for inclusion into computer aided diagnostic prompting system

Among signs of breast cancer encountered in digital mammograms radiologists point to microcalcification clusters (MCCs). Their detection is a challenging problem from both medical and image processing point of views. This work presents two concurrent methods for MCC detection, and studies their possible inclusion to a computer aided diagnostic prompting system. One considers Wavelet Domain Hidden Markov Tree (WHMT) for modeling microcalcification edges. The model is used for differentiation between MC and non-MC edges based on the weighted maximum likelihood (WML) values. The classification of objects is carried out using spatial filters. The second method employs SUSAN edge detector in the spatial domain for mammogram segmentation. Classification of objects as calcifications is carried out using another set of spatial filters and Feedforward Neural Network (NN). A same distance filter is employed in both methods to find true clusters. The analysis of two methods is performed on 54 image regions from the mammograms selected randomly from DDSM database, including benign and cancerous cases as well as cases which can be classified as hard cases from both radiologists and the computer perspectives. WHMT/WML is able to detect 98.15% true positive (TP) MCCs under 1.85% of false positives (FP), whereas the SUSAN/NN method achieves 94.44% of TP at the cost of 1.85% for FP. The comparison of these two methods suggests WHMT/WML for the computer aided diagnostic prompting. It also certifies the low false positive rates for both methods, meaning less biopsy tests per patient