Masses In Mammograms Research Articles

Visual enhancement of microcalcifications and masses in digital mammograms using modified multifractal analysis

Microcalcifications and masses, as breast tissue anomalies (deviations from observed background regularity), may be viewed as statistically rare occurrences in a mammogram image. After recognizing their principal common features - bright image parts not belonging to the surrounding tissue, with significant local contrast just around the edges - several modifications to multifractal image analysis have been introduced. Starting from a mammogram image, the proposed method creates corresponding multifractal images. Additional post-processing, based on mathematical morphology, refines the procedure by selecting and outlining only regions with possible microcalcifications and masses. The proposed method was tested through referent mammograms from the MiniMIAS database. In all cases involving the said database, the method has successfully enhanced declared anomalies: microcalcifications and masses. The results obtained have shown that the described procedure may provide visual assistance to radiologists in clinical mammogram examinations or be used as a preprocessing step for further mammogram processing, such as segmentation, classification, and automatic detection of suspected bright breast tissue lesions.

Effect of BIRADS shape descriptors on breast cancer analysis

This paper focuses on an approach for characterising the spatial structure of mammogram masses using various multimodal features. Experiments have been conducted on benchmark digital database for screening mammography (DDSM) database with 300 mammograms. According to Breast Imaging Reporting and Data System (BIRADS) spatial structure of mammogram masses can be discriminated using its shape, size and density properties. Various new geometrical shape, margin and texture features and DDSM descriptors are used to characterise the morphology of masses as either benign or malignant. The effect of DDSM descriptors on classification accuracy is analysed. The experimental results shows that all 20 features including DDSM descriptors produce better accuracy of 93.3& compared to 86.7& without using DDSM descriptors. The overall Wilk's lambda value is reduced when all 20 features are used for classification. The proposed features set using shape, texture and DDSM descriptors provide better (c/v) ratio compared to wavelet, Gabor and other features reported in the literature.

A bilateral analysis scheme for false positive reduction in mammogram mass detection

In this paper, a bilateral image analysis scheme is developed for the purpose of reducing false positives (FPs) in the detection of masses in dense mammograms. It consists of two steps: a region matching step for determining the correspondence between a pair of mammograms, and a bilateral similarity analysis step for discarding FPs in the detection. For the first step, a matching cost is defined to quantify the credibility of the corresponding region in a pair of bilateral mammograms. For the second step, a similarity measurement is introduced to discriminate between mass and normal for a pair of bilateral regions based on both global and local image appearances. The proposed scheme is tested on a set of 332 mammograms. The results show that the proposed scheme could obtain better performance when compared with several existing bilateral analysis schemes. With detection sensitivity at 85%, the proposed bilateral scheme could reduce the FP rate of a unilateral scheme from 3.64 to 2.39 per image, a 34% reduction.

A Review on Textural Features Based Computer Aided Diagnostic System for Mammogram Mass Classification Using GLCM & RBFNN

A Review on Textural Features Based Computer Aided Diagnostic System for Mammogram Mass Classification Using GLCM & RBFNN

Computerised segmentation of suspicious lesions in the digital mammograms

In this paper, a robust marker-controlled watershed method is proposed to yield more accurate segmentation results to delineate the masses in mammograms. The proposed method consists of three main steps: pre-processing, marker extraction and the final segmentation. In the first step, pre-processing algorithm is developed using top-hat morphological filter, wavelet transform followed by a noise smoothing anisotropic diffusion filter. In the second step, a novel technique is developed for the extraction of robust markers to locate the accurate position of the suspicious lesions. Finally, the extracted markers are used within the watershed algorithm to allow the reliable segmentation and quantification of masses in mammograms. The developed computer method was quantitatively evaluated using the area overlap metric (AOM), average minimum Euclidean distance (AMED) and Hausdorff distance (HD). The mean ± standard deviation values of AOM, AMED and HD for our method are 0.83 ± 0.10, 1.49 ± 1.20 mm and 4.62 ± 0.80 mm. We compared our method with previously developed marker-controlled watershed algorithm with respect to the manual segmentation performed by an expert radiologist. Experimental results demonstrate that our method has a strong potential to be used as an aid to radiologists in the interpretation of screening mammograms.

Computer-aided diagnosis of mammographic masses using scalable image retrieval.

Computer-aided diagnosis of masses in mammograms is important to the prevention of breast cancer. Many approaches tackle this problem through content-based image retrieval techniques. However, most of them fall short of scalability in the retrieval stage, and their diagnostic accuracy is, therefore, restricted. To overcome this drawback, we propose a scalable method for retrieval and diagnosis of mammographic masses. Specifically, for a query mammographic region of interest (ROI), scale-invariant feature transform (SIFT) features are extracted and searched in a vocabulary tree, which stores all the quantized features of previously diagnosed mammographic ROIs. In addition, to fully exert the discriminative power of SIFT features, contextual information in the vocabulary tree is employed to refine the weights of tree nodes. The retrieved ROIs are then used to determine whether the query ROI contains a mass. The presented method has excellent scalability due to the low spatial-temporal cost of vocabulary tree. Extensive experiments are conducted on a large dataset of 11553 ROIs extracted from the digital database for screening mammography, which demonstrate the accuracy and scalability of our approach.

Detection of Mass in Digital Mammograms

Detection of Mass in Digital Mammograms

Automatic Detection of Masses in Mammograms Using Quality Threshold Clustering, Correlogram Function, and SVM.

Breast cancer is the second most common type of cancer in the world. Several computer-aided detection and diagnosis systems have been used to assist health experts and to indicate suspect areas that would be difficult to perceive by the human eye; this approach has aided in the detection and diagnosis of cancer. The present work proposes a method for the automatic detection of masses in digital mammograms by using quality threshold (QT), a correlogram function, and the support vector machine (SVM). This methodology comprises the following steps: The first step is to perform preprocessing with a low-pass filter, which increases the scale of the contrast, and the next step is to use an enhancement to the wavelet transform with a linear function. After the preprocessing is segmentation using QT; then, we perform post-processing, which involves the selection of the best mass candidates. This step is performed by analyzing the shape descriptors through the SVM. For the stage that involves the extraction of texture features, we used Haralick descriptors and a correlogram function. In the classification stage, the SVM was again used for training, validation, and final test. The results were as follows: sensitivity 92.31 %, specificity 82.2 %, accuracy 83.53 %, mean rate of false positives per image 1.12, and area under the receiver operating characteristic (ROC) curve 0.8033. Breast cancer is notable for presenting the highest mortality rate in addition to one of the smallest survival rates after diagnosis. An early diagnosis means a considerable increase in the survival chance of the patients. The methodology proposed herein contributes to the early diagnosis and survival rate and, thus, proves to be a useful tool for specialists who attempt to anticipate the detection of masses.

Mammogram Mass Classification Using Support Vector Machine with Texture, Shape Features and Hierarchical Centroid Method

Mammogram Mass Classification Using Support Vector Machine with Texture, Shape Features and Hierarchical Centroid Method

Benign and malignant breast tumors classification based on region growing and CNN segmentation

Breast cancer is regarded as one of the most frequent mortality causes among women. As early detection of breast cancer increases the survival chance, creation of a system to diagnose suspicious masses in mammograms is important. In this paper, two automated methods are presented to diagnose mass types of benign and malignant in mammograms. In the first proposed method, segmentation is done using an automated region growing whose threshold is obtained by a trained artificial neural network (ANN). In the second proposed method, segmentation is performed by a cellular neural network (CNN) whose parameters are determined by a genetic algorithm (GA). Intensity, textural, and shape features are extracted from segmented tumors. GA is used to select appropriate features from the set of extracted features. In the next stage, ANNs are used to classify the mammograms as benign or malignant. To evaluate the performance of the proposed methods different classifiers (such as random forest, naïve Bayes, SVM, and KNN) are used. Results of the proposed techniques performed on MIAS and DDSM databases are promising. The obtained sensitivity, specificity, and accuracy rates are 96.87%, 95.94%, and 96.47%, respectively.

Development and validation of a fully automated system for detection and diagnosis of mammographic lesions.

We present a comprehensive and fully automated system for computer-aided detection and diagnosis of masses in mammograms. Novel methods for detection include: selection of suspicious focal areas based on analysis of the gradient vector field, rejection of oriented components of breast tissue using multidirectional Gabor filtering, and use of differential features for rejection of false positives (FPs) via clustering of the surrounding fibroglandular tissue. The diagnosis step is based on extraction of contour-independent features for characterization of lesions as benign or malignant from automatically detected circular and annular regions. A new unified 3D free-response receiver operating characteristic framework is introduced for global analysis of two binary categorization problems in cascade. In total, 3,080 suspicious focal areas were extracted from a set of 156 full-field digital mammograms, including 26 malignant tumors, 120 benign lesions, and 18 normal mammograms. The proposed system detected and diagnosed malignant tumors with a sensitivity of 0.96, 0.92, and 0.88 at, respectively, 1.83, 0.46, and 0.45 FPs/image, with two stages of stepwise logistic regression for selection of features, a cascade of Fisher linear discriminant analysis and an artificial neural network with radial basis functions, and leave-one-patient-out cross-validation.

An efficient fractal method for detection and diagnosis of breast masses in mammograms.

In this paper, we present an efficient fractal method for detection and diagnosis of mass lesion in mammogram which is one of the abnormalities in mammographic images. We used 110 images that were carefully selected by a radiologist, and their abnormalities were also confirmed by biopsy. These images included circumscribed benign, ill-defined, and spiculated malignant masses. Firstly, we discriminated lesions automatically using new fractal dimensions. The results which were examined by different types of breast density showed that the proposed method was able to yield quite satisfactory detection results. Secondly, noting that contours of masses playing the most important role in diagnosis of different mass types, we defined new fractal features based on information extraction from the contours. This information is able to identify the roughness in mass contours and determines the extent of spiculation or smoothness of the masses. In this manner, in classification of the spiculated malignant masses from the circumscribed benign tumors, we achieved highly satisfactory results, i.e., 0.98 measured in terms of area under ROC curve (AUC). In this paper, it is also shown that the roughness in contours is a suitable characteristic feature for diagnosis of ill-defined malignant tumors with AUC equal to 0.94 in their classification. The extracted information was also found to be useful in the classification of early malignancies whereas in the classification of spiculated and ill-defined malignant masses in their early stage from those of benign tumors, we achieved high accuracy of 0.99 and 0.90 for AUC, respectively.

Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features

The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainee's likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502-0.739, 95% Confidence Interval: 0.543-0.680,p < 0.002). Patterns in detection errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees.

WAVELET TRANSFORMATION-BASED DETECTION OF MASSES IN DIGITAL MAMMOGRAMS

A Novel Wavelet Transformation-Based Detection of Masses in digital mammograms (WTBDM) is proposed in this paper that enables for the early prognosis of breast cancer. The wavelet analysis is explored for analyzing and identifying strong variations in intensities within the mammographic data which highlights and recognizes the masses effectively. The proposed algorithm, in addition to wavelet transformation, uses morphological preprocessing, region properties and seeded region growing to remove the digitization noises, to remove the pectoral muscle and to suppress radiopaque artifacts, thus segmenting the abnormal masses accurately. The combined potential of wavelet and region growing helps for effective mass segmentation that vouches the merit of the proposed technique.

Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm

In Brazil, the National Cancer Institute (INCA) reports more than 50,000 new cases of the disease, with risk of 51 cases per 100,000 women. Radiographic images obtained from mammography equipments are one of the most frequently used techniques for helping in early diagnosis. Due to factors related to cost and professional experience, in the last two decades computer systems to support detection (Computer-Aided Detection – CADe) and diagnosis (Computer-Aided Diagnosis – CADx) have been developed in order to assist experts in detection of abnormalities in their initial stages. Despite the large number of researches on CADe and CADx systems, there is still a need for improved computerized methods. Nowadays, there is a growing concern with the sensitivity and reliability of abnormalities diagnosis in both views of breast mammographic images, namely cranio-caudal (CC) and medio-lateral oblique (MLO). This paper presents a set of computational tools to aid segmentation and detection of mammograms that contained mass or masses in CC and MLO views. An artifact removal algorithm is first implemented followed by an image denoising and gray-level enhancement method based on wavelet transform and Wiener filter. Finally, a method for detection and segmentation of masses using multiple thresholding, wavelet transform and genetic algorithm is employed in mammograms which were randomly selected from the Digital Database for Screening Mammography (DDSM). The developed computer method was quantitatively evaluated using the area overlap metric (AOM). The mean±standard deviation value of AOM for the proposed method was 79.2±8%. The experiments demonstrate that the proposed method has a strong potential to be used as the basis for mammogram mass segmentation in CC and MLO views. Another important aspect is that the method overcomes the limitation of analyzing only CC and MLO views.

Mass retrieval in mammogram based on hashing theory and linear neighborhood propagation

Mass detection in mammograms usually has high false positive (FP) rate. Content based mass retrieval can effectively reduce the FP rate by comparing the image which is to be determined with mass images which have already been diagnosed. In this paper, a method combining discriminating anchor graph hashing (DAGH) and linear neighborhood propagation (LNP) is proposed for mammogram mass retrieval. Original AGH image representation does not consider pathological relevance in defining image similarity. To solve this problem, DAGH is put forward as a new image representation, which introduces the pathological class into image similarity. Furthermore, LNP is employed as a relevance feedback technique. Finally, interactive retrieval for mammogram masses is implemented based on the learning strategy between the underlying features and high-level semantic for images. Mammograms provided by the Breast Center of Peking University People's Hospital (BCPKUPH) are used to test the proposed method. Experimental results show that the DAGH image representation introducing pathological class is superior to original AGH in analyzing the similarity of mass images. Compared with existing methods, the proposed method shows obvious improvement in mass retrieval performance.

Performance Analysis of Image Segmentation Methods for the Detection of Masses in Mammograms

ABSTRACT Detection and quantification of breast cancer is a very critical step in mammograms and therefore, needs an accurate and standard technique for breast tumor segmentation. In the last four decades, a number of algorithms have been published in the literature. Each one has their own merits and demerits. The aim of this paper is to make a comparative analysis of the most promising methods, namely fuzzy c-means (FCM), k-means (KM), marker controlled watershed segmentation (MCWS) and region growing (RG), for the detection and segmentation of masses in mammographic images on real data obtained from Metro Hospital. Robustness of the methods is demonstrated by validating their quantitative results with expert manual data. It is observed that the RG gives better results compared to three other methods. Keywords Breast cancer, mathematical morphology, marker controlled watershed segmentation, region growing. 1. INTRODUCTION Cancer is one of the leading causes of human mortality in the world. The most common type of cancer in women is the breast cancer. Early detection and diagnosis leads to the successful treatment and thus play the key role in controlling the breast cancer deaths. Hence, it is essential for the women of age group 30-40 years to have regular screening every year. Currently, X-ray mammography is considered to be the most simple and reliable imaging method for the early detection of breast cancer [1]. Presence of masses or micro-calcification clusters on mammograms is considered to be preliminary indicators for early stage breast cancer. To determine the tumor area, in most of the hospitals, a radiologist performs the diagnosis of breast tumor manually on mammographic images. Visual examination of large volume of mammograms and shortage of experienced radiologists makes the process error prone and time consuming. Computer-aided diagnosis (CAD) system may help radiologist and doctors in reliable and precise diagnosis of breast cancer [2]. Numerous techniques have been developed and proposed as an emerging tool to segment masses from surrounding tissues in digital mammograms. Martins

Detection and Identification of Mass Structure in Digital Mammogram

Breast Cancer is the rampant issue facing by most of the women these days. Mammography is the most successful modus operandi for verdict of breast cancer. Radiologists view mammograms to perceive the abnormalities. In this paper, we urbanized an algorithm to isolate and extract the malignant masses in mammograms for detection of breast cancer. This exertion is based on the following course of action: (a) Confiscate the background information from the DICOM image. (b)Refurbish the RGB image to gray image (c) Apply thresholding to remove the background information (ROI). (d)Apply median filter on the image to reduce random noise and preserve the edges. (e)Extract the binary image contours. (f)Carry out Area-open on the image and. (g)To end with act upon filling on the resultant image. This method was tested over the real time images of various patients taken from a cancer hospital and implemented using Matlab code. Thus, capable in executing the pre-processed image efficiently and detected the segmentation region which helped in retrieving the mass present in the digital mammogram and hence documented the malignant mass by calculating different features like Area, perimeter, Compactness, Width, Height, Isotropic factor and Eccentricity of the extracted mass.

Sclerosing Lymphocytic Lobulitis Manifesting as Suspicious Microcalcifications with Hashimoto's Thyroiditis in a Young Woman

Sclerosing lymphocytic lobulitis (SLL) is a rare inflammatory disorder, which is also known as fibrous mastopathy and lymphocytic mastitis. It is commonly associated with autoimmune disorders, particularly type 1 diabetes and thyroiditis. We report the case of a 28-year-old woman diagnosed as SLL with Hashimoto's thyroiditis, but without diabetes. She presented suspicious microcalcifications without palpable mass in routine mammograms in both breasts. She had been diagnosed as Hashimoto's thyroiditis several years before and had been followed up in endo-clinics.

Directionlet transform based sharpening and enhancement of mammographic X-ray images

Due to difficulty in detecting the low contrast and noisy nature of X-ray mammography images, they have to be enhanced to obtain a clear and good view. Though Sharpening Technique (ST) is used to enhance the contrast, it introduces noise in the enhancement process, and they do not include anisotropic features. This paper proposes a ST, which uses multiscale linear and anisotropic geometrical features obtained from directionlet transform (DT). The newly formulated method that combines multidirectional geometrical information has various tunable parameters and improved noise control by means of multiscale features. The DT that uses skewed and elongated directional basis functions not only captures the point singularities, but also links them into linear structure. The performance of the proposed DT ST is compared with non-linear unsharp masking (NLUSM). While the DT and LoG based sharpened images are given to the input of standard AHE, their performance is improved. Enhancement Measure and structural similarity measure are used to analyze the performance of the proposed method. Though the images are enhanced, the quality of the image is not degraded. As a specific application, the enhanced images are used to detect the microcalcification and spiculated masses in mammograms.