Annie F. Frere

Automatic identification of the pectoral muscle in mammograms

The pectoral muscle represents a predominant density region in most medio-lateral oblique (MLO) views of mammograms; its inclusion can affect the results of intensity-based image processing methods or bias procedures in the detection of breast cancer. Local analysis of the pectoral muscle may be used to identify the presence of abnormal axillary lymph nodes, which may be the only manifestation of occult breast carcinoma. We propose a new method for the identification of the pectoral muscle in MLO mammograms based upon a multiresolution technique using Gabor wavelets. This new method overcomes the limitation of the straight-line representation considered in our initial investigation using the Hough transform. The method starts by convolving a group of Gabor filters, specially designed for enhancing the pectoral muscle edge, with the region of interest containing the pectoral muscle. After computing the magnitude and phase images using a vector-summation procedure, the magnitude value of each pixel is propagated in the direction of the phase. The resulting image is then used to detect the relevant edges. Finally, a post-processing stage is used to find the true pectoral muscle edge. The method was applied to 84 MLO mammograms from the Mini-MIAS (Mammographic Image Analysis Society, London, U.K.) database. Evaluation of the pectoral muscle edge detected in the mammograms was performed based upon the percentage of false-positive (FP) and false-negative (FN) pixels determined by comparison between the numbers of pixels enclosed in the regions delimited by the edges identified by a radiologist and by the proposed method. The average FP and FN rates were, respectively, 0.58% and 5.77%. Furthermore, the results of the Gabor-filter-based method indicated low Hausdorff distances with respect to the hand-drawn pectoral muscle edges, with the mean and standard deviation being 3.84/spl plusmn/1.73 mm over 84 images.

Analysis of asymmetry in mammograms via directional filtering with Gabor wavelets

This paper presents a procedure for the analysis of left-right (bilateral) asymmetry in mammograms. The procedure is based upon the detection of linear directional components by using a multiresolution representation based upon Gabor wavelets. A particular wavelet scheme with two-dimensional Gabor filters as elementary functions with varying tuning frequency and orientation, specifically designed in order to reduce the redundancy in the wavelet-based representation, is applied to the given image. The filter responses for different scales and orientation are analyzed by using the Karhunen-Loeve (KL) transform and Otsus method of thresholding. The KL transform is applied to select the principal components of the filter responses, preserving only the most relevant directional elements appearing at all scales. The selected principal components, thresholded by using Otsus method, are used to obtain the magnitude and phase of the directional components of the image. Rose diagrams computed from the phase images and statistical measures computed thereof are used for quantitative and qualitative analysis of the oriented patterns. A total of 80 images from 20 normal cases, 14 asymmetric cases, and six architectural distortion cases from the Mini-MIAS (Mammographic Image Analysis Society, London, U.K.) database were used to evaluate the scheme using the leave-one-out methodology. Average classification accuracy rates of up to 74.4% were achieved.

Identification of the breast boundary in mammograms using active contour models

A method for the identification of the breast boundary in mammograms is presented. The method can be used in the preprocessing stage of a system for computeraided diagnosis (CAD) of breast cancer and also in the reduction of image file size in picture archiving and communication system applications. The method started with modification of the contrast of the original image. A binarisation procedure was then applied to the image, and the chain-code algorithm was used to find an approximate breast contour. Finally, the identification of the true breast boundary was performed by using the approximate contour as the input to an active contour model algorithm specially tailored for this purpose. After demarcation of the breast boundary, all artifacts outside the breast region were eliminated. The method was applied to 84 medio-lateral oblique mammograms from the Mini-MIAS database. Evaluation of the detected breast boundary was performed based upon the percentage of false-positive and false-negative pixels determined by a quantitative comparison between the contours identified by a radiologist and those identified by the proposed method. The average false positive and false negative rates were 0.41% and 0.58%, respectively. The two radiologists who evaluated the results considered the segmentation results to be acceptable for CAD purposes.

Segmentation of the fibro-glandular disc in mammograms using Gaussian mixture modelling.

The paper presents a technique for the segmentation of the fibro-glandular disc in mammograms based upon a statistical model of breast density. The density function of the model was represented by a mixture of up to four weighted Gaussians, each one corresponding to a specific density class in the breast. The parameters of the model and the number of tissue classes in the breast were determined using the expectation-maximisation algorithm and the minimum description length method. Grey-level statistics of the pectoral muscle were used to determine the tissue categories that are likely to represent the fibro-glandular disc. The method was applied to 84 medio-lateral oblique mammograms from the Mini-MIAS database. The results of the segmented fibro-glandular disc were assessed by a radiologist using the original and the segmented images, with reference to a ranking table categorising the results of segmentation as: 1: excellent; 2: good; 3: average; 4: poor; and 5: complete failure. Of the 84 cases analysed, 64.3% were rated as excellent, 16.7% were rated as good, 10.7% were rated as average, and 4.7% were rated as poor; only 3.6% of the cases were rated as a complete failure with regard to segmentation of the fibro-glandular disc.

Directional analysis of images with Gabor wavelets

The paper presents a new scheme for analysis of linear directional components in images by using a multiresolution representation based on Gabor wavelets. A dictionary of Gabor filters with varying tuning frequency and orientation, specifically designed in order to reduce the redundancy in the wavelet-based representation, is applied to the given image. The filter responses for different scales and orientation are analyzed by using the Karhunen-Loeve (KL) transform and Otsus (1979) method of thresholding. The KL transform is applied to select the principal components of the filter responses, preserving only the most relevant directional elements appearing at all scales. The first N principal components, thresholded by using Otsus method, are used to reconstruct the magnitude and phase of the directional components of the image. Rose diagrams computed from the phase images are used for quantitative and qualitative analysis of the oriented patterns. The proposed scheme is applied to the analysis of asymmetry between left and right mammograms. For this purpose, a set of three features is extracted from the Rose diagrams and used in a parametric statistical classifier. A total of 80 images from 20 normal cases, 14 asymmetric cases, and 6 distortion cases from the Mini-MIAS database were used to evaluate the scheme using the leave-one-out methodology, resulting in an average diagnostic accuracy of 72.5%.

Detection and characterization of microcalcifications in mammographic images

Describes a mathematical method to detect and to characterize microcalcifications in mammographic images, using the Hough transform. This method will allow radiologists not only to examine digitized and filtered images, but also to take advantage of a method which will assist them in their diagnoses. Identification of ring-shaped and vermiculated microcalcifications associated to non-malignant and malignant tumors is provided.

Evaluating the focal spot MTF in all field locations by computer simulation

This paper proposes a method of evaluating x-ray tube focal spots and the corresponding image sharpness by computer simulation based on the transfer functions theory. This theory was chosen due to its quantitative as well as qualitative response for the radiographic systems performance, which provides less subjective evaluations and better predictions about the characteristics of the imaging process. The present method uses as input data the effective focal spot dimensions in the field center and the value of the target angulation. An ideal pinhole which scans the entire radiation field is simulated. It allows to obtain the point spread function (PSFs) for any region of interest. The modulation transfer functions (MTFs) are then determined from 2D Fourier transformation from the PSFs. This provides to evaluate the focal spot projection in all field locations and therefore to predict the sharpness of the associated image. Furthermore the computer simulation reduces greatly the number of practical procedures required for obtaining the data which provides the MTF evaluation of radiographic systems.

Computer simulation technique to preview the influence of the recording system on the image sharpness in mammography

Following procedures used to simulate the image sharpness along the radiation field based on X-rays geometric exposure developed in previous work, here we describe another computer simulation procedure intended to evaluate the influence of any recording system as radiographic film or screen-film combinations, on the image sharpness in mammography. In this current work we take into account the parameters from the recording system besides the radiation projection from the focal spot in order to yield a simulated image on the computer screen relative to the expected image to be obtained in actual conditions with a singular recording system for a singular mammography equipment. The focal spot sizes in all field locations, as well as the respectives intensity distributions, the sensitometric curve for a radiographic film or for a screen-film combination, and also the screen intensifying factor, conversion efficiency, absorption factor and emission spectrum were used as input parameters for the simulation. Simulated images were compared to those obtained with actual mammographic equipment, by using a resolution phantom, and both types of images were in good agreement. The main advantage of this procedure will be the possibility of predicting the image sharpness characteristics for any mammography equipment with any type of recording system without exposure tests.

Computerized simulation X-ray focus appraisement

This paper introduces computerized simulation to find the configuration of the focal spot of radiology systems in different locations of the X-ray field. Results of simulations were compared to tests made in different radiology units. Comparison between simulated and experimental data were in good agreement, therefore confirming that the proposed simulation model is appropriate.

Analysis of Bilateral Asymmetry in Mammograms via Directional Filtering with Gabor Wavelets

Most of the concepts used in image processing and computer vision for oriented pattern analysis have their roots in neurophysiological studies of the mammalian visual system. Campbell and Robson suggested that the human visual system decomposes retinal images into a number of filtered images, each of which contains intensity variations over a narrow range of frequency and orientation. Marcelja, and Jones and Palmer demonstrated that simple cells in the primary visual cortex have receptive fields that are restricted to small regions of space and are highly structured, and that their behavior corresponds to local measurements of frequency. According to Daugman, one suitable model for the 2D receptive field profiles measured experimentally in mammalian cortical simple cells is the parameterized family of 2D Gabor functions. Jones and Palmer and Daugman showed that a majority of cortical cells have 2D receptive field profiles that can be fitted well, in the sense of a statistical test, by members of the family of 2D Gabor elementary functions. Another important characteristic of Gabor functions or filters is their optimal joint resolution in both space and frequency, which suggests that Gabor filters are appropriate operators for tasks requiring simultaneous measurement in the two domains. Except for the optimal joint resolution possessed by the Gabor functions, the difference of Gaussian (DOG) and difference of offset Gaussian (DOOG) filters used by Malik and Perona have similar properties. Gabor filters have been presented in several works on image processing; however, most of these works are related to segmentation and analysis of texture. Rolston and Rangayyan, and Rolston proposed methods for directional decomposition and analysis of linear components in images using multiresolution Gabor filters. Multiresolution analysis using Gabor filters has natural and desirable properties for analysis of directional information in images; most of these properties are based on biological vision studies as described previously. Other multiresolution techniques have also been used with success in addressing related topics such as texture analysis and segmentation, and image enhancement. Chang and Kuo, for instance, developed a new method for texture classification that uses a tree-structured wavelet transform for decomposing an image. In their work, image decomposition is performed by taking into account the energy of each subimage instead of decomposing subsignals in the low-frequency channels. If the energy of a subimage is higher than a certain fixed threshold value C, then the decomposition procedure is applied again; otherwise, the decomposition is stopped in that region.