Homero Schiabel

Online Mammographic Images Database for Development and Comparison of CAD Schemes

Considering the difficulties in finding good-quality images for the development and test of computer-aided diagnosis (CAD), this paper presents a public online mammographic images database free for all interested viewers and aimed to help develop and evaluate CAD schemes. The digitalization of the mammographic images is made with suitable contrast and spatial resolution for processing purposes. The broad recuperation system allows the user to search for different images, exams, or patient characteristics. Comparison with other databases currently available has shown that the presented database has a sufficient number of images, is of high quality, and is the only one to include a functional search system.

Contrast enhancement in dense breast images to aid clustered microcalcifications detection.

This paper presents a method to provide contrast enhancement in dense breast digitized images, which are difficult cases in testing of computer-aided diagnosis (CAD) schemes. Three techniques were developed, and data from each method were combined to provide a better result in relation to detection of clustered microcalcifications. Results obtained during the tests indicated that, by combining all the developed techniques, it is possible to improve the performance of a processing scheme designed to detect microcalcification clusters. It also allows operators to distinguish some of these structures in low-contrast images, which were not detected via conventional processing before the contrast enhancement. This investigation shows the possibility of improving CAD schemes for better detection of microcalcifications in dense breast images.

Contrast enhancement in dense breast images using the modulation transfer function

This work proposes a method aimed at enhancing the contrast in dense breast images in mammography. It includes a new preprocessing technique, which uses information on the modulation transfer function (MTF) of the mammographic system in the whole radiation field. The method is applied to improve the efficiency of a computer-aided diagnosis (CAD) scheme. Seventy-five regions of interest (ROIs) from dense mammograms were acquired in two pieces of equipment (a CGR Senographe 500t and a Philips Mammodiagnost) and were digitized in a Lumiscan 50 laser scanner. A computational procedure determines the effective focal spot size in each region of interest from the measured focal spot in the center for a given mammographic equipment. Using computational simulation the MTF is then calculated for each field region. A procedure that enlarges the high-frequency portion of this function is applied and a convolution between the resulting new function and the original image is performed. Both original and enhanced images were submitted to a processing procedure for detecting clustered microcalcifications in order to compare the performance for dense breast images. ROIs were divided into four groups, two for each piece of equipment-one with clustered microcalcifications and another without microcalcifications. Our results show that in about 10% of the enhanced images more signals were detected when compared to the results for the original dense breast images. This is important because the usual processing techniques used in CAD schemes present poor results when applied to dense breast images. Since the MTF method is a well-recognized tool in the evaluation of radiographic systems, this new technique could be used to associate quality assurance procedures with the processing schemes employed in CAD for mammography.

Segmentation technique for detecting suspect masses in dense breast digitized images as a tool for mammography CAD schemes

Breast cancer is one of the most important cause to mortality rate among women. Computer-Aided Detection (CAD) schemes have been developed as a tool in detecting early breast cancer. This can be an important tool in mammography since previous studies have been indicated that the detection of breast cancer can be increased up to 20% when assisted by a CAD scheme. One of the main stages of such process is thus the segmentation of structures of interest, as the suspect masses. However, when evaluating mammograms obtained from dense breasts, a CAD scheme efficacy can be very reduced due to the poor contrast of such type of image. This work attempts hence to this challenge, by describing a methodology for segmenting suspect masses in dense breast images as a part of a CAD scheme under development. This methodology is based on the Watershed transformation, which is combined with two other procedures -- a histogram equalization, working as pre-processing for enhance images contrast, and a labeling procedure intended to reduce noise. Tests with a set of 252 regions of interest extracted from 130 digitized mammograms have registered a scheme sensibility of 92% with about 90% of specificity. These results are promising when applied to dense breast images, which can improve significantly the performance of a processing scheme for such type of cases in mammography.

Investigation of clustered microcalcification features for an automated classifier as part of a mammography CAD scheme

Classification of breast microcalcifications and clusters depends characteristics selected to be the input for an automated classifier. Artificial neural networks have been used to aid in classification of structures on mammograms images. However, to achieve the classification, some attributes have to be adequately extracted from the images in the database used for tests. As a part of a CAD scheme in development, our intention is to establish a ANN-based classifier, intended to distribute detected clustered microcalcifications in one of 5 classes, according to BI-RADS classification (normal, benign, probably benign, suspicious and probably malignant). This work reports a part of this procedure, by extracting and selecting most of significant characteristics regarding digitized mammography images containing clustered microcalcifications. Two distinct classes-probably benign and suspicious-are considered in order to compare the selected characteristics incidence distribution. Distance between both classes could be estimated by using Gaussian curves. Images used for the tests were from a database composed by mammograms digitized with 600 dpi of spatial resolution in a andbit grayscale. The regions of interest were selected based on physicians reports on the existence of a cluster. This study has shown that characteristics just as irregularity, number of microcalcifications in a cluster, and cluster area are already enough to separate the processed images in two very distinct classes-suspicious and probably benign, although other features could be necessary for a more detailed classification.

Automatic segmentation of breast masses using enhanced ICA mixture model

Abstract Image segmentation is considered, among all the stages of image processing, the most critical stage of data processing, because a good classification is dependent on the features extracted from the segmented images. In this work, we are proposing to use the technique called Enhanced ICA Mixture Model (EICAMM) for automatic segmentation of breast masses, aiming to comparing it to other segmentation methods known for segmentation of medical images such as Watershed, Self-Organizing Map (SOM), K-means and Fuzzy C-means techniques. For the analysis of the results, it was used Jaccard similarity measure for comparing the result obtained by the segmentation techniques with that one obtained by an expert opinion. All images considered in this work were segmented and then analyzed by us to improve the segmentation performed by an expert and to detect lesion shape for further classification. These models have been applied for the segmentation of suspicious masses in digital mammographic images, including images of dense breasts. The obtained results show a good performance of EICAMM that was the unique technique able to detect masses in dense breast interest region in preprocessed images and in original images. In this way, EICAMM could be considered as a good alternative approach to be applied for breast masses classification.

Filtering of poisson noise in digital mammography using local statistics and adaptive wiener filter

A novel image denoising algorithm has been proposed for quantum noise reduction in digital mammography. The method uses the Anscombe transformation to stabilize noise variance and convert the signal-dependent Poisson noise into an approximately signal-independent Gaussian additive noise. In the Anscombe domain, noise is removed through an adaptive Wiener filter, whose parameters are obtained considering local image statistics. Thus, the method does not require any a priori knowledge about the original signal, because all the necessary parameters are estimated directly from the noisy image. The method was applied on synthetic mammograms generated based upon an anthropomorphic software breast phantom with different levels of simulated quantum noise. The evaluation of the proposed method was performed by calculating the peak signal-to-noise ratio (PSNR) and the mean structural similarity index (MSSIM) before and after denoising. Results show that the proposed algorithm improves image quality by reducing image noise without significantly affecting image sharpness.

Performance of a processing scheme for clustered microcalcifications detection with different images database

Although many researchers have reported high efficacy rates of some computer-aided diagnosis (CAD) schemes, it is known that their performance depends strongly on the database used for the tests. This is an important task to the comparison of different schemes performance, since they usually are developed and tested with a particular images set. Previously, we have reported the development of an image processing scheme designed to detect clustered microcalcifications as a part of a CAD scheme. Therefore, in this work we are reporting the performance results of such a processing procedure after tests with three different image databases: two corresponding to mammograms obtained from Hospital das Clinicas de Ribeirao Preto, Brazil, but from different units and digitizied in different scanners; and a third corresponding to mammographic images obtained directly in digital form by Internet from the National Expert and Training Centre for Breast Cancer Screening at the University of Nijmegen, the Netherlands. As expected, the scheme efficacy in detecting clusters has changed according to the images set tested: from 95% of efficacy (true positive plus true negative results) for the best situation to 88% for the worst one. In addition, we discuss briefly some factors relative to the images sets which can change a CAD scheme results.

Mammography Images Restoration by Quantum Noise Reduction and Inverse MTF Filtering

This work proposes a new restoration method to improve mammographic images by using Anscombe Transform and Wiener Filter to quantum noise reduction. Besides, it is performed an image enhancement by using a restoration inverse filter, calculated based on the image system modulation transfer function (MTF). This pre-processing technique were used for a set of mammographic phantom images in order measure the number of micro calcifications correctly detected by a computer-aided detection (CAD) algorithm. Results showed that the proposed method improved breast images quality by overcome the acquisition process constrains and reducing noise.The performance of the breast microcalcification CAD was improved when using the restored images set in comparison to the original one.

A method to contrast enhancement of digital dense breast images aimed to detect clustered microcalcifications

Computer-aided diagnosis (CAD) schemes have been developed in many research centers to help the early detection of breast cancer. However, dense breast images are a challenge to CAD schemes due to the low contrast between structures of interest (such as microcalcifications-small size structures-which usually are associated to several breast tumors) and the background. This work describes a method to eliminate the background of a digitized mammogram image as well as two specific techniques to enhance the contrast in dense breast digital images as part of a CAD scheme under development in our group. The results indicate that these techniques can improve the performance of the scheme, and, thus, it can help in the early detection of breast cancer.