Digital Mammogram Images Research Articles

FCSOFM Technique for Medical Image Segmentation

Medical image segmentation is a challenging task. This paper proposed FCSOFM a novel technique for medical image segmentation. FCSOFM technique segments the defected region of brain MRI and digital mammogram images. Then we calculate the efficiency of our proposed technique by the computation of confusion matrix.

Robust Automatic Pectoral Muscle Segmentation from Mammograms Using Texture Gradient and Euclidean Distance Regression.

In computer-aided diagnosis (CAD) of mediolateral oblique (MLO) view of mammogram, the accuracy of tissue segmentation highly depends on the exclusion of pectoral muscle. Robust methods for such exclusions are essential as the normal presence of pectoral muscle can bias the decision of CAD. In this paper, a novel texture gradient-based approach for automatic segmentation of pectoral muscle is proposed. The pectoral edge is initially approximated to a straight line by applying Hough transform on Probable Texture Gradient (PTG) map of the mammogram followed by block averaging with the aid of approximated line. Furthermore, a smooth pectoral muscle curve is achieved with proposed Euclidean Distance Regression (EDR) technique and polynomial modeling. The algorithm is robust to texture and overlapping fibro glandular tissues. The method is validated with 340 MLO views from three databases-including 200 randomly selected scanned film images from miniMIAS, 100 computed radiography images and 40 full-field digital mammogram images. Qualitatively, 96.75% of the pectoral muscles are segmented with an acceptable pectoral score index. The proposed method not only outperforms state-of-the-art approaches but also accurately quantifies the pectoral edge. Thus, its high accuracy and relatively quick processing time clearly justify its suitability for CAD.

Detection of Microcalcification Using the Wavelet Based Adaptive Sigmoid Function and Neural Network

Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and graylevel co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.

Empirical Evaluation of Generic Weighted Cubicle Pattern and LBP Derivatives for Abnormality Detection in Mammogram Images

Digital image processing techniques are very useful in abnormality detection in digital mammogram images. Nowadays, texture-based image segmentation of digital mammogram images is very popular due to its better accuracy and precision. Local binary pattern (LBP) descriptor has attracted many researchers working in the field of texture analysis of digital images. Because of its success, many texture descriptors have been introduced as variants of LBP. In this work, we propose a novel texture descriptor called generic weighted cubicle pattern (GWCP) and we analyzed the proposed operator for texture image classification. We also performed abnormality detection through mammogram image segmentation using k-Nearest Neighbors (KNN) algorithm and compared the performance of the proposed texture descriptor with LBP and other variants of LBP namely local ternary pattern (LTPT), extended local texture pattern (ELTP) and local texture pattern (LTPS). For evaluation, we used the performance metrics such as accuracy, error rate, sensitivity, specificity, under estimation fraction and over estimation fraction. The results prove that the proposed method outperforms other descriptors in terms of abnormality detection in mammogram images.

Quantitative analysis of morphological techniques for automatic classification of micro-calcifications in digitized mammograms

In this paper we present an evaluation of four different algorithms based on Mathematical Morphology, to detect the occurrence of individual micro-calcifications in digitized mammogram images from the mini-MIAS database. A morphological algorithm based on contrast enhancement operator followed by extended maxima thresholding retrieved most of micro-calcifications. In order to reduce the number of false positives produced in that stage, a set of features in the spatial, texture and spectral domains was extracted and used as input in a support vector machine (SVM). Results provided by TMVA (Toolkit for Multivariate Analysis) produced the ranking of features that allowed discrimination between real micro-calcifications and normal tissue. An additional parameter, that we called Signal Efficiency*Purity (denoted SE*P), is proposed as a measure of the number of micro-calcifications with the lowest quantity of noise. The SVM with Gaussian kernel was the most suitable for detecting micro-calcifications. Sensitivity was obtained for the three types of breast. For glandular, it detected 137 of 163 (84.0%); for dense tissue, it detected 74 of 85 (87.1%) and for fatty breast, it detected 63 of 71 (88.7%). The overall sensitivity was 85.9%. The system also was tested in normal images, producing an average of false positives per image of 13 in glandular tissue, 11 in dense tissue and 15 in fatty tissue.

Low energy mammogram obtained in contrast-enhanced digital mammography (CEDM) is comparable to routine full-field digital mammography (FFDM)

PurposeContrast enhanced digital mammography (CEDM) uses low energy and high energy exposures to produce a subtracted contrast image. It is currently performed with a standard full-field digital mammogram (FFDM). The purpose is to determine if the low energy image performed after intravenous iodine injection can replace the standard FFDM. MethodsAnd Materials: In an IRB approved HIPAA compatible study, low-energy CEDM images of 170 breasts in 88 women (ages 26–75; mean 50.3) undergoing evaluation for elevated risk or newly diagnosed breast cancer were compared to standard digital mammograms performed within 6 months. Technical parameters including posterior nipple line (PNL) distance, compression thickness, and compression force on the MLO projection were compared. Mammographic findings were compared qualitatively and quantitatively. Mixed linear regression using generalized estimating equation (GEE) method was performed. Intraclass correlation coefficients (ICC) with 95% confidence interval (95%CI) were estimated to assess agreement. ResultsNo statistical difference was found in the technical parameters compression thickness, PNL distance, compression force (p-values: 0.767, 0.947, 0.089). No difference was found in the measured size of mammographic findings (p-values 0.982–0.988). Grouped calcifications had a mean size/extent of 2.1cm (SD 0.6) in the low-energy contrast images, and a mean size/extent of 2.2cm (SD 0.6) in the standard digital mammogram images. Masses had a mean size of 1.8cm (SD 0.2) in both groups. Calcifications were equally visible on both CEDM and FFDM. ConclusionLow energy CEDM images are equivalent to standard FFDM despite the presence of intravenous iodinated contrast. Low energy CEDM images may be used for interpretation in place of the FFDM, thereby reducing patient dose.

Auto-Identification of Pectoral Muscle Region in Digital Mammogram Images

Auto-Identification of Pectoral Muscle Region in Digital Mammogram Images

Curvelet image denoising of mammogram images

Mammography, the most commonly used diagnostic technique is used for early detection of breast cancer. As mammograms are low contrast and noisy images, it is essential to reduce noise while preserving fine details and edges. In order to obtain efficient diagnosis, a constructive analysis curvelet is used to provide optimal sparse representation of smooth objects and edges. Hence, in this work, a novel noise reduction method using curvelet transform is proposed for digital mammogram images. Here, digital curvelet transform is applied to the mammogram images which are embedded in random, salt and pepper, Poisson, speckle and Gaussian noises. As curvelet coefficients occur at all scales, locations and orientations, proposed thresholding algorithm applied at the expansions lead to better denoising. And it also offers exact reconstruction of images that exhibit higher perceptual quality in recovery of edges. The PSNR values of the enhanced images are higher than related earlier techniques.

REGION-BASED CONTRAST ENHANCEMENT OF DIGITAL MAMMOGRAMS USING AN IMPROVED WATERSHED SEGMENTATION

A simple edge-based preprocessing scheme is proposed in this paper for contrast enhancement of digital mammogram images while preserving the edges more accurately. This proposed method has three steps: (i) initially the breast region is segmented from the mammogram images by removing the film artifacts, (ii) the pectoral muscle region is identified and excluded from the breast region using a novel adaptive thresholding method, and (iii) an Improved Watershed Segmentation (IWS) is applied to segment the breast profile, and each region is enhanced with simple histogram equalization. The segmentation is performed in order to achieve adaptive contrast enhancement. The performance of this proposed pectoral removal method is analyzed with two measures: Hausdorff Distance (HD) and Mean of Absolute Error Distance (MAED), and the proposed contrast enhancement approach is been analyzed with the five diverse parameters along with the classification accuracy. The experiments and results show the potential performance of our proposed algorithm over the existing approaches with optimum results on all the performance measure and the classification performance is been evaluated with a hybrid neural network, our proposed method proves the better performance with the achievement of 92% accuracy.

Feature extraction method for breast cancer diagnosis in digital mammograms using multi-resolution transformations and SVM-fuzzy logic classifier

The paper comprises a pattern to study digital mammogram images, which is mainly oriented to the recognition of the malignant/benign masses, skin thickening and micro calcifications. Image processing algorithms, like multi-resolution transformations, are implemented to obtain vector coefficients; wherein, a matrix is obtained on the basis of wavelet coefficients. Texture features are extracted from the wavelet coefficients. Dynamic thresholds are applied to optimise the number of features, and achieve maximum classification accuracy rate. The SVM-fuzzy method is used to classify between normal and abnormal tissues. The fuzzy classifier is used for extracting geometrical features. Due to lack of generalisations, the neuro-fuzzy rule is integrated with a kernel SVM to form a support vector based on the neural network in handling the uncertainty information. We use the Mammographic Image Analysis Society MIAS standard data set for the study and training-set purpose. We obtain classification accuracy rates of about 93.9%, demonstrating the proposed method for contribution towards a successful diagnosis pattern for breast cancer.

Neuro-Fuzzy Approach to Microcalcification Contrast Enhancement in Digitized Mammogram Images

Computer aided diagnoses can assists radiologists in detecting microcalcification, crucial evidence in mammogram for the early diagnosis of breast cancer. A novel approach is proposed in this paper for early detection of breast cancer by enhancing microcalcification regions in mammogram images using hybrid neuro-fuzzy technique. As a first stage, the mammogram intensities are fuzzified using three linguistic labels. Then, the inference engine of a classical fuzzy system is replaced by a collection of sixteen parallel neural networks and a cascade neural network in order to reduce the computational time for real-time applications. The parallel cascade neural networks are trained using data sets that randomly selected from the original fuzzy decision matrix. Finally, the value of the local mask centre is enhanced after defuzzification the input sets. This work is extensively evaluated using two different types of resources which are Mammographic Image Analysis Society database (MIAS) and University of South Florida (USF) database. As a result, it found to be sensitive in enhancing the microcalcifications regions in mammogram with very little number false positive regions.

Removal of Pectoral Muscle Region in Digital Mammograms using Binary Thresholding

The pectoral muscle represents a predominant density region in Medio-Lateral Oblique (MLO) views of mammograms, which appears at approximately the same density as the dense tissues of interest in the image and can affect the results of image analysis methods. Therefore, segmentation of pectoral muscle is important in order to limit the search for the breast abnormalities only to the breast region. In this paper, a simple and effective approach is proposed to exclude the pectoral muscle based on binary operation. The performance is analyzed by the Hausdorff Distance Measure (HDM) and also the Mean of Absolute Error Distance Measure (MAEDM) based on differences between the results received from the radiologists and by the proposed method. The digital mammogram images are taken from MIAS dataset which contains 322 images in total, out of which the proposed algorithm able to detect and remove the pectoral region from 291 images successfully.

Integration of Fuzzy and Wavelet Approaches towards Mammogram Contrast Enhancement

Mammography is the most reliable, effective, low cost and high sensitive method for early detection of breast cancer. In breast cancer diagnosis, it is difficult for radiologists to detect the typical diagnostic signs because mammograms are low contrast and noisy images. In order to improve diagnosis accuracy, mammogram contrast enhancement technique is often used to enhance the contrast of mammogram and aid the radiologists. In this paper, a combined approach with fuzzy and wavelet towards improved enhancement of details and subtle features in digital mammogram images. The proposed contrast enhancement approach utilizes wavelet transform to decompose the mammogram, and then approximation coefficients are modified by fuzzy contrast enhancement approach and detail coefficients are by non-linear transformation. After that, inverse wavelet transform is applied on modified coefficients to obtain the enhanced mammogram. The proposed algorithm has been tested on a number of images in Digital Database for Screening Mammography (DDSM), comparing the results with histogram equalization which is a well-established image enhancement technique and Cheng’s enhancement algorithm based on wavelet transform. In order to accurately assess the proposed approach, enhanced mammogram images are evaluated through objective image quality assessment parameters: information entropy, contrast and peak signal-to-noise ratio (PSNR). Experimental results of the proposed approach are quite promising.

Unsupervised feature selection in digital mammogram image using rough set theory

Feature Selection (FS) is a process which attempts to select features which are more informative. In this paper, a novel unsupervised FS in mammogram images, using rough set-based relative dependency measures, is proposed. A typical mammogram image processing system generally consists of mammogram image acquisition, pre-processing of image, segmentation and features extraction from the segmented mammogram image. The proposed unsupervised FS method is used to select features from data sets; the method is compared with existing rough set based supervised FS methods, and the classification performance of both methods are recorded and demonstrate the efficiency of this method.

Entropy based unsupervised Feature Selection in digital mammogram image using rough set theory

Feature Selection (FS) is a process, which attempts to select features, which are more informative. In the supervised FS methods various feature subsets are evaluated using an evaluation function or metric to select only those features, which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised FS. However, in unsupervised learning, decision class labels are not provided. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this paper, a novel unsupervised FS in mammogram image, using rough set-based entropy measures, is proposed. A typical mammogram image processing system generally consists of mammogram image acquisition, pre-processing of image, segmentation, features extracted from the segmented mammogram image. The proposed method is used to select features from data set, the method is compared with the existing rough set-based supervised FS methods and classification performance of both methods are recorded and demonstrates the efficiency of the method.

A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation

This paper presents a method for breast cancer diagnosis in digital mammogram images. Multiresolution representations, wavelet or curvelet, are used to transform the mammogram images into a long vector of coefficients. A matrix is constructed by putting wavelet or curvelet coefficients of each image in row vector, where the number of rows is the number of images, and the number of columns is the number of coefficients. A feature extraction method is developed based on the statistical t-test method. The method is ranking the features (columns) according to its capability to differentiate the classes. Then, a dynamic threshold is applied to optimize the number of features, which can achieve the maximum classification accuracy rate. The method depends on extracting the features that can maximize the ability to discriminate between different classes. Thus, the dimensionality of data features is reduced and the classification accuracy rate is improved. Support vector machine (SVM) is used to classify between the normal and abnormal tissues and to distinguish between benign and malignant tumors. The proposed method is validated using 5-fold cross validation. The obtained classification accuracy rates demonstrate that the proposed method could contribute to the successful detection of breast cancer.

MRT letter: Segmentation and texture‐based classification of breast mammogram images

Breast cancer is the most common cancer diagnosed among women. In this article, support vector machine is used to classify digital mammogram images into malignant and benign. Wiener filter is used to handle the possible quantum noise, which is more likely to occur in mammograms. Stack-based connected component method is proposed for background removal, and the image is enhanced using retinax method. Seeded region growing algorithm is used to remove the pectoral muscle part of the mammogram. We have extracted 13 different multidomains' features for classification. Results show the superiority of the proposed algorithm in terms of sensitivity, specificity, and accuracy. We have used MIAS database of mammography for experimentation.

Directional features for automatic tumor classification of mammogram images

One way for breast cancer diagnosis is provided by taking radiographic (X-ray) images (termed mammograms) for suspect patients, images further used by physicians to identify potential abnormal areas thorough visual inspection. When digital mammograms are available, computer-aided based diagnostic may help the physician in having a more accurate decision. This implies automatic abnormal areas detection using segmentation, followed by tumor classification. This work aims at describing an approach to deal with the classification of digital mammograms. Patches around tumors are manually extracted to segment the abnormal areas from the remaining of the image, considered as background. The mammogram images are filtered using Gabor wavelets and directional features are extracted at different orientation and frequencies. Principal Component Analysis is employed to reduce the dimension of filtered and unfiltered high-dimensional data. Proximal Support Vector Machines are used to final classify the data. Superior mammogram image classification performance is attained when Gabor features are extracted instead of using original mammogram images. The robustness of Gabor features for digital mammogram images distorted by Poisson noise with different intensity levels is also addressed.

Characterization of microcalcification: can digital monitor zooming replace magnification mammography in full-field digital mammography?

The aim of this study was to compare the diagnostic accuracy and image quality of microcalcifications in zoomed digital contact mammography with digital magnification mammography. Three radiologists with different levels of experience in mammography reviewed 120 microcalcification clusters in 111 patients with a full-field digital mammography system relying on digital magnification mammogram (MAG) images and zoomed images from contact mammography (ZOOM) using commercially available zooming systems on monitors. Each radiologist estimated the probability of malignancy and rated the image quality and confidence rate. Performance was evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and receiver operating characteristic (ROC) analysis. All three radiologists rated MAG images higher than ZOOM images for sensitivity with statistical significance (average value, 92% vs. 87%, P<0.05) and performance by ROC analysis improved with MAG imaging. The confidence rate for diagnosis decision and the assessment of lesion characteristics were also better in MAG images than in ZOOM images with statistical significance (P<0.0001). Digital magnification mammography can enhance diagnostic performance when characterizing microcalcifications. Images zoomed from digital contact mammography cannot serve as an alternative to direct magnification digital mammography.