Segmentation of suspicious densities in digital mammograms

Brake, G.M. te; Karssemeijer, Nico

doi:10.1118/1.1339884

Cited by 76 publications

(55 citation statements)

References 11 publications

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“…Although automatic analysis of mammograms cannot fully replace radiologists, an accurate computer-aided analysis method can help radiologists to make more reliable and efficient decisions [9]. Tumors and other abnormalities present in the mammograms are of basic interests that need to be segmented and extracted in mammograms [10]- [11]. Some of the grayscale based segmentation methods are quite effective to extract the exact edges of homogeneous grayscale regions.…”

Section: Introductionmentioning

confidence: 99%

Detection of Tumours in Digital Mammograms Using Wavelet Based Adaptive Windowing Method

Sreeja¹,

Rathika²,

Devaraj³

2012

IJMECS

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Abstract-Mammography is the most effective procedure for the early detection of breast diseases. Mammogram analysis refers the processing of mammograms with the goal of finding abnormality presented in the mammogram. In this paper, the tumour can be detected by using wavelet based adaptive windowing technique. Coarse segmentation is the first step which can be done by using wavelet based histogram thresholding where, the thereshold value is chosen by performing 1-D wavelet based analysis of PDFs of wavelet transformed images at different channels. Fine segmentation can be done by partitioning the image into fixed number of large and small windows. By calculating the mean, maximum and minimum pixel values for the windows a threshold value has been obtained. Depending upon the threshold values the suspicious areas have been segmented. Intensity adjustment is applied as a preprocessing step to improve the quality of an image before applying the proposed technique. The algorithm is validated with mammograms in Mammographic Image Analysis Society Mini Mammographic database which shows that the proposed technique is capable of detecting lesions of very different sizes.Index Terms-wavelet based Thresholding, breast cancer, mammography, window based Thresholding, segmentation.

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Section: Introductionmentioning

confidence: 99%

Detection of Tumours in Digital Mammograms Using Wavelet Based Adaptive Windowing Method

Sreeja¹,

Rathika²,

Devaraj³

2012

IJMECS

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show abstract

“…The common purpose of segmentation is to group the homogeneous pixels into regions with respect to specific features and semantic content. Segmentation step through this research aims to extract the lesion border from the healthy skin [28]. For accurate segmenation process and correct detection for lesion boundary, image thrsholding step should follow contrast adjustment.…”

Section: A Image Segmentationmentioning

confidence: 99%

Automated Imaging System for Pigmented Skin Lesion Diagnosis

Ahmed¹,

Sharwy²,

Mai³

2016

ijacsa

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Abstract-Through the study of pigmented skin lesions risk factors, the appearance of malignant melanoma turns the anomalous occurrence of these lesions to annoying sign. The difficulty of differentiation between malignant melanoma and melanocytic naive is the error-bone problem that usually faces the physicians in diagnosis. To think through the hard mission of pigmented skin lesions diagnosis different clinical diagnosis algorithms were proposed such as pattern analysis, ABCD rule of dermoscopy, Menzies method, and 7-points checklist. Computerized monitoring of these algorithms improves the diagnosis of melanoma compared to simple naked-eye of physician during examination. Toward the serious step of melanoma early detection, aiming to reduce melanoma mortality rate, several computerized studies and procedures were proposed. Through this research different approaches with a huge number of features were discussed to point out the best approach or methodology could be followed to accurately diagnose the pigmented skin lesion. This paper proposes automated system for diagnosis of melanoma to provide quantitative and objective evaluation of skin lesion as opposed to visual assessment, which is subjective in nature. Two different data sets were utilized to reduce the effect of qualitative interpretation problem upon accurate diagnosis. Set of clinical images that are acquired from a standard camera while the other set is acquired from a special dermoscopic camera and so named dermoscopic images. System contribution appears in new, complete and different approaches presented for the aim of pigmented skin lesion diagnosis. These approaches result from using large conclusive set of features fed to different classifiers. The three main types of different features extracted from the region of interest are geometric, chromatic, and texture features. Three statistical methods were proposed to select the most significant features that will cause a valuable effect in diagnosis; Fisher score method, t-test, and F-test. The selected features of high-ranking score based on the statistical methods are used for the diagnosis of the two lesion groups using Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) as three different classifiers proposed. The overall System performance was then measured in regards to Specificity, Sensitivity and Accuracy. According to the different approaches that will be mentioned later the best result was showen by the ANN designed with the feature selected according to fisher score method enables a diagnostic accuracy of 96.25% and 97% for dermoscopic and clinical images respectively.

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“…Active contour segmentation has been used in medical imaging [13][14][15][16]. In breast imaging, Brake et al used a discrete active contour method to segment mammographic mass lesions [17]. Sahiner et al incorporated edge and region analysis to help minimize the contour energy [18].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2013

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Dedicated breast CT (bCT) produces highresolution 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 (computeraided detection/diagnosis) methods depends on sufficient segmentation of lesions. Thus, in this study, we present a 3D lesion segmentation method for breast masses in contrastenhanced 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.

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Segmentation of suspicious densities in digital mammograms

Cited by 76 publications

References 11 publications

Detection of Tumours in Digital Mammograms Using Wavelet Based Adaptive Windowing Method

Detection of Tumours in Digital Mammograms Using Wavelet Based Adaptive Windowing Method

Automated Imaging System for Pigmented Skin Lesion Diagnosis

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

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