Breast Mass Segmentation Research Articles

A STUDY OF REGION BASED SEGMENTATION METHODS FOR MAMMOGRAMS

Breast Cancer is one of the most common diseases th at are found in women. The number of women getting affected by cancer is increasing year by year. Detecting cancer in the la te stages, leads to very complicated surgeries and the chance of death is very high. Early detection of Breast Cancer helps in less comp licated procedures and early recovery. Many tests h ave been found so as to detect cancer. Some of these tests are mammography; ultras ound etc.Mammography is a method that helps in earl y detection of Breast cancer. But finding the mass and its spread from a mammographic image is very difficult. Expert radiol ogists are needed for accurate reading of a mammogram. Researchers have been worki ng for years for algorithms that help for easy dete ction and segmentation of breast masses. Feature extraction and classificatio n have also been done extensively so that the studi ed cases can be compared to diagnose the new cases. Segmentation of cancerous m ass regions from the breast tissues is a difficult process. Many algorithms have been proposed for this. Some of these algorithms ar e region growing, watershed segmentation, clusterin g etc. Region Growing Method is based on two major factors which is the spoint selection and then the stopping criteria. Watershed Segmentation on the other hand is based on the basic geographical conce pt of watersheds and catchment basins, and uses a t echnique called as flooding. A study of these two major region based methods such as Region Growing and Watershed Segmentation are co mpared and detailed in this paper.

Level Set Segmentation of Breast Masses in Contrast-Enhanced Dedicated Breast CT and Evaluation of Stopping Criteria

Dedicated breast CT (bCT) produces high-resolution 3D tomographic images of the breast, fully resolving fibroglandular tissue structures within the breast and allowing for breast lesion detection and assessment in 3D. In order to enable quantitative analysis, such as volumetrics, automated lesion segmentation on bCT is highly desirable. In addition, accurate output from CAD (computer-aided detection/diagnosis) methods depends on sufficient segmentation of lesions. Thus, in this study, we present a 3D lesion segmentation method for breast masses in contrast-enhanced bCT images. The segmentation algorithm follows a two-step approach. First, 3D radial-gradient index segmentation is used to obtain a crude initial contour, which is then refined by a 3D level set-based active contour algorithm. The data set included contrast-enhanced bCT images from 33 patients containing 38 masses (25 malignant, 13 benign). The mass centers served as input to the algorithm. In this study, three criteria for stopping the contour evolution were compared, based on (1) the change of region volume, (2) the average intensity in the segmented region increase at each iteration, and (3) the rate of change of the average intensity inside and outside the segmented region. Lesion segmentation was evaluated by computing the overlap ratio between computer segmentations and manually drawn lesion outlines. For each lesion, the overlap ratio was averaged across coronal, sagittal, and axial planes. The average overlap ratios for the three stopping criteria ranged from 0.66 to 0.68 (dice coefficient of 0.80 to 0.81), indicating that the proposed segmentation procedure is promising for use in quantitative dedicated bCT analyses.

Automatic segmentation of breast masses using enhanced ICA mixture model

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.

Breast mass segmentation using region-based and edge-based methods in a 4-stage multiscale system

Mass segmentation in mammograms is a challenging task due to problems such as low contrast, ill-defined, fuzzy or spiculated borders, and the presence of intensity inhomogeneities. These facts complicate the development of computer-aided diagnosis (CAD) systems to assist radiologists. In this paper, a novel mass segmentation algorithm for mammograms based on robust multiscale feature-fusion, and automatic estimation based maximum a posteriori (MAP) method is presented. The proposed segmentation technique consists of mainly four stages: a dynamic contrast improvement scheme applied to a selected region-of-interest (ROI), background-influence correction by template matching, detection of mass candidate points by prior and posterior probabilities based on robust multiscale feature-fusion, and final delineation of the mass region by a MAP scheme. This segmentation method is applied to 480 ROI masses that used ground truth from two radiologists. To compare its effectiveness with the state-of-the-art segmentation methods, three statistical metrics are employed. The experimental results indicate that the developed methods can reliably segment ill-defined or spiculated lesions when compared to other algorithms. Its integration in a CAD system may result in an improved aid to radiologists.

Breast Mass Segmentation Using Iterated Graph Cuts Based on Multi-Scale Smoothing

A novel scheme for mass segmentation in mammography is proposed,which is based on Graph Cuts algorithm and multi-scale analysis.Mammogram is segmented by statistical region merging firstly,and the obtained rough contour is used as the initial contour for Graph Cuts segmentation.In iterative optimization stage of the algorithm,multi-scale analysis method is introduced to estimate the Gaussian Mixture Model(GMM)parameters with pyramidal decomposing serial images instead of fix-scale original image.The algorithm estimates GMM parameters rapidly with fewer samples by utilizing the complementarities between segmentation accuracy of fine scale and segmentation easiness of coarse scale.In order to improve efficiency of the proposed approach,watershed algorithm is utilized to produce a region adjacency graph,replacing pixel adjacency graph with fewer samples.The proposed method,interactive Graph Cuts and GrabCut were simultaneously applied for mass segmentation on 110 mammographic ROIs,and the achieved average ratios of misclassification error are 1.57,3.46 and 5.01 respectively.The results demonstrate that the proposed method achieves a better performance in accuracy and robustness.

Hybrid Segmentation of Mass in Mammograms Using Template Matching and Dynamic Programming

Accurate image segmentation for breast lesions is a critical step in computer-aided diagnosis systems. The objective of this study was to develop a robust method for the automatic segmentation of breast masses on mammograms to extract feasible features for computer-aided diagnosis systems. The data set used in this study consisted of 483 regions of interest extracted from 328 patients. A hybrid method for segmenting breast masses was proposed on the basis of the template-matching and dynamic programming techniques. First, a template-matching technique was used to locate and obtain the rough region of masses. Then, on the basis of this rough region, a local cost function for dynamic programming was defined. Finally, the optimal contour was derived by applying dynamic programming as an optimization technique. The performance of this proposed segmentation method was evaluated using area-based and boundary distance-based similarity measures based on radiologists' manually marked annotations. A comparison with three different segmentation algorithms on the data set was provided. The mean overlap percentage for our proposed hybrid method was 0.727 ± 0.127, whereas those for Timp and Karssemeijer's dynamic programming method, Song et al's plane-fitting and dynamic programming method, and the normalized cut segmentation method were 0.657 ± 0.216, 0.636 ± 0.190, and 0.562 ± 0.199, respectively. All P values for the measure distribution of our proposed method and the other three algorithms were <.001. A hybrid method based on the template-matching and dynamic programming techniques was proposed to segment breast masses on mammograms. Evaluation results indicate that the proposed segmentation method can improve the accuracy of mass segmentation compared to three other algorithms. The proposed segmentation method shows better performance and has great potential in improving the accuracy of computer-aided diagnosis systems in interpreting mammograms.

Breast Mass Segmentation in Mammography Using Plane Fitting and Dynamic Programming

Segmentation is an important and challenging task in a computer-aided diagnosis (CAD) system. Accurate segmentation could improve the accuracy in lesion detection and characterization. The objective of this study is to develop and test a new segmentation method that aims at improving the performance level of breast mass segmentation in mammography, which could be used to provide accurate features for classification. This automated segmentation method consists of two main steps and combines the edge gradient, the pixel intensity, as well as the shape characteristics of the lesions to achieve good segmentation results. First, a plane fitting method was applied to a background-trend corrected region-of-interest (ROI) of a mass to obtain the edge candidate points. Second, dynamic programming technique was used to find the "optimal" contour of the mass from the edge candidate points. Area-based similarity measures based on the radiologist's manually marked annotation and the segmented region were employed as criteria to evaluate the performance level of the segmentation method. With the evaluation criteria, the new method was compared with 1) the dynamic programming method developed by Timp and Karssemeijer, and 2) the normalized cut segmentation method, based on 337 ROIs extracted from a publicly available image database. The experimental results indicate that our segmentation method can achieve a higher performance level than the other two methods, and the improvements in segmentation performance level were statistically significant. For instance, the mean overlap percentage for the new algorithm was 0.71, whereas those for Timp's dynamic programming method and the normalized cut segmentation method were 0.63 (P < .001) and 0.61 (P < .001), respectively. We developed a new segmentation method by use of plane fitting and dynamic programming, which achieved a relatively high performance level. The new segmentation method would be useful for improving the accuracy of computerized detection and classification of breast cancer in mammography.

A new automated method for the segmentation and characterization of breast masses on ultrasound images

Segmentation is one of the first steps in most computer-aided diagnosis systems for characterization of masses as malignant or benign. In this study, the authors designed an automated method for segmentation of breast masses on ultrasound (US) images. The method automatically estimated an initial contour based on a manually identified point approximately at the mass center. A two-stage active contour method iteratively refined the initial contour and performed self-examination and correction on the segmentation result. To evaluate the method, the authors compared it with manual segmentation by two experienced radiologists (R1 and R2) on a data set of 488 US images from 250 biopsy-proven masses (100 malignant and 150 benign). Two area overlap ratios (AOR1 and AOR2) and an area error measure were used as performance measures to evaluate the segmentation accuracy. Values for AOR1, defined as the ratio of the intersection of the computer and the reference segmented areas to the reference segmented area, were 0.82 +/- 0.16 and 0.84 +/- 0.18, respectively, when manually segmented mass regions by R1 and R2 were used as the reference. Although this indicated a high agreement between the computer and manual segmentations, the two radiologists' manual segmentation results were significantly (p < 0.03) more consistent, with AOR1 = 0.84 +/- 0.16 and 0.91 +/- 0.12, respectively, when the segmented regions by R1 and R2 were used as the reference. To evaluate the segmentation method in terms of lesion classification accuracy, feature spaces were formed by extracting texture, width-to-height, and posterior shadowing features based on either automated computer segmentation or the radiologists' manual segmentation. A linear discriminant analysis classifier was designed using stepwise feature selection and two-fold cross validation to characterize the mass as malignant or benign. For features extracted from computer segmentation, the case-based test A(z) values ranged from 0.88 +/- 0.03 to 0.92 +/- 0.02, indicating a comparable performance to those extracted from manual segmentation by radiologists (A(z) value range: 0.87 +/- 0.03 to 0.90 +/- 0.03).

Improved dynamic‐programming‐based algorithms for segmentation of masses in mammograms

In this paper, two new boundary tracing algorithms for segmentation of breast masses are presented. These new algorithms are based on the dynamic programming-based boundary tracing (DPBT) algorithm proposed in Timp and Karssemeijer, [S. Timp and N. Karssemeijer, Med. Phys. 31, 958-971 (2004)] The DPBT algorithm contains two main steps: (1) construction of a local cost function, and (2) application of dynamic programming to the selection of the optimal boundary based on the local cost function. The validity of some assumptions used in the design of the DPBT algorithm is tested in this paper using a set of 349 mammographic images. Based on the results of the tests, modifications to the computation of the local cost function have been designed and have resulted in the Improved-DPBT (IDPBT) algorithm. A procedure for the dynamic selection of the strength of the components of the local cost function is presented that makes these parameters independent of the image dataset. Incorporation of this dynamic selection procedure has produced another new algorithm which we have called ID2PBT. Methods for the determination of some other parameters of the DPBT algorithm that were not covered in the original paper are presented as well. The merits of the new IDPBT and ID2PBT algorithms are demonstrated experimentally by comparison against the DPBT algorithm. The segmentation results are evaluated with base on the area overlap measure and other segmentation metrics. Both of the new algorithms outperform the original DPBT; the improvements in the algorithms performance are more noticeable around the values of the segmentation metrics corresponding to the highest segmentation accuracy, i.e., the new algorithms produce more optimally segmented regions, rather than a pronounced increase in the average quality of all the segmented regions.

Why should breast tumour detection go three dimensional?

Although x-ray mammography is widely developed for breast tumour detection, it suffers from spatial superposition in its two-dimensional (2D) representation of a three-dimensional (3D) breast structure. Accordingly, 3D breast imaging, such as cone-beam computed tomography (CT), arises at the historic moment. In this paper, we theoretically elucidate the spatial superposition effect associated with x-ray mammography on breast tumour detection. This explanation is based on the line integral of x-ray traversing a composite breast model. As a result, we can characterize the difficulty of detecting small tumours in terms of local intensity contrast in x-ray images. In comparison, we also introduce cone-beam CT breast imaging for 3D breast volume representation, which offers advantages for breast mass segmentation and measurement. The discussion is demonstrated with an experiment with a breast surgical specimen. In conclusion, we strongly believe that 3D volumetric representation allows for more accurate breast tumour detection.

Interactive segmentation of masses in digitized mammograms: observer variability and discriminative applications

This work presents a study on interactive segmentation of breast masses in digitized mammograms and on the analysis of shape parameters: compactness ( C), contour moment-based shape parameter ( M), and Fourier shape parameter ( F). Digitized mammograms were selected for this study from The University of South Florida Digital Database for Screening Mammography (DDSM), containing 94 masses (46 benign and 48 malignant) with different types of margins. Two observers participated in this study. Each observer performed the same procedure of segmentation with the whole set of images three times in three independent sessions. Our results show a high dependence of M on the shape of the segmented image and more sensitivity of C and F to contour irregularities. Intra- and inter-observer differences in human segmentation, measured by means of the shape parameters, are shown. Each single shape parameter allows differentiating between malignant and benign masses. When results from both observers are used together, the degree of discrimination is higher than when they are used independently. We have demonstrated also various degrees of discrimination for other characteristics of the masses such as assessment, margins and shape. As a conclusion, the use of several manually outlined contours as a gold standard for CAD systems can improve performance.

Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms.

As an ongoing effort to develop a computer aid for detection of masses on mammograms, we recently designed an object-based region-growing technique to improve mass segmentation. This segmentation method utilizes the density-weighted contrast enhancement (DWCE) filter as a pre-processing step. The DWCE filter adaptively enhances the contrast between the breast structures and the background. Object-based region growing was then applied to each of the identified structures. The region-growing technique uses gray-scale and gradient information to adjust the initial object borders and to reduce merging between adjacent or overlapping structures. Each object is then classified as a breast mass or normal tissue based on extracted morphological and texture features. In this study we evaluated the sensitivity of this combined segmentation scheme and its ability to reduce false positive (FP) detections on a data set of 253 digitized mammograms, each of which contained a biopsy-proven breast mass. It was found that the segmentation scheme detected 98% of the 253 biopsy-proven breast masses in our data set. After final FP reduction, the detection resulted in 4.2 FP per image at a 90% true positive (TP) fraction and 2.0 FPs per image at an 80% TP fraction. The combined DWCE and object-based region growing technique increased the initial detection sensitivity, reduced merging between neighboring structures, and reduced the number of FP detections in our automated breast mass detection scheme.