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Computerized Scheme for Histological Classification of Masses with Architectural Distortions in Ultrasonographic Images  [PDF]
Akiyoshi Hizukuri, Ryohei Nakayama, Emi Honda, Yumi Kashikura, Tomoko Ogawa
Journal of Biomedical Science and Engineering (JBiSE) , 2016, DOI: 10.4236/jbise.2016.98035
Abstract: Architectural distortion is an important ultrasonographic indicator of breast cancer. However, it is difficult for clinicians to determine whether a given lesion is malignant because such distortions can be subtle in ultrasonographic images. In this paper, we report on a study to develop a computerized scheme for the histological classification of masses with architectural distortions as a differential diagnosis aid. Our database consisted of 72 ultrasonographic images obtained from 47 patients whose masses had architectural distortions. This included 51 malignant (35 invasive and 16 non-invasive carcinomas) and 21 benign masses. In the proposed method, the location of the masses and the area occupied by them were first determined by an experienced clinician. Fourteen objective features concerning masses with architectural distortions were then extracted automatically by taking into account subjective features commonly used by experienced clinicians to describe such masses. The k-nearest neighbors (k-NN) rule was finally used to distinguish three histological classifications. The proposed method yielded classification accuracy values of 91.4% (32/35) for invasive carcinoma, 75.0% (12/16) for noninvasive carcinoma, and 85.7% (18/21) for benign mass, respectively. The sensitivity and specificity values were 92.2% (47/51) and 85.7% (18/21), respectively. The positive predictive values (PPV) were 88.9% (32/36) for invasive carcinoma and 85.7% (12/14) for noninvasive carcinoma whereas the negative predictive values (NPV) were 81.8% (18/22) for benign mass. Thus, the proposed method can help the differential diagnosis of masses with architectural distortions in ultrasonographic images.
Dual Modality: Mammogram and Ultrasound Feature Level Fusion for Characterization of Breast Mass  [PDF]
Minavathi,Dr. Murali. S.,,Dr. Dinesh M.S.
International Journal of Innovative Technology and Exploring Engineering , 2013,
Abstract: detection of abnormalities in breast is done in different phases using different modalities and different biomedical techniques. These techniques and modalities are able to furnish morphological, metabolic and functional information of breast. Integrating these information assists in clinical decision making. But it is difficult to retrieve all these information from single modality. Multimodal techniques supply complementary information for improved therapy planning. This work concentrates on early detection of breast cancer which characterises the breast mass as malignant or benign by investigating the features retrieved from dual modalities: mammograms and Ultrasound. Architectural distortion (AD) with Spiculated mass is an important finding for the early detection of breast cancer. Such distortions can be classified as spiculation, retraction, and distortion which can be detected in mammograms. Spiculated masses carry a much higher risk of malignancy than calcifications or other types of masses. The proposed approach is based on the fusion of two modalities at feature extraction level with Z-Score Normalization technique to improve the performance of dual modality. Gabor filters are used to retrieve texture features from region of interest (ROI) of mammograms. Shape and structural features are retrieved from ROI’s of Ultrasound. In addition to that some other discriminative features like denseness texture feature, standard deviation, entropy and homogeneity are also extracted from ROI’s of both modalities. Feature level fusion is then achieved by using a simple concatenation rule. Finally classification is done using Support vector machine (SVM) classifiers to classify breast mass as malignant or benign. Receiver operating characteristic curves (ROC) are used to evaluate the performance. SVM classifiers achieved 95.6% sensitivity in characterising the breast masses using the features retrieved from two modalities.
International Journal of Machine Intelligence , 2011,
Abstract: Detection and classification of spiculated masses in ultrasound images is still a challenge due to the interference of speckle noise and fuzziness of boundaries. Ultrasound (US) is an important adjunct to mammography in breast cancer detection as it doubles the rate of detection in dense breasts do a dynamic analysis of moving structures in breast. This paper presents technique to detect spiculations and boundary of spiculated masses in breast ultrasound images. In the proposed method, ultrasound images are preprocessed using Gaussian smoothing to remove additive noise and anisotropic diffusion filters to remove multiplicative noise (speckle noise). Active contour method has been used to extract a closed contour of filtered image which is the boundary of the spiculated mass. Spiculations which make breast mass unstructured or irregular are marked by measuring the angle of curvature of each pixel at the boundary of mass. To classify the breast mass as malignant or benign we have used the structure of mass in accordance with spiculations and elliptical shape. We have used receiver operating characteristic curve (ROC) to evaluate the performance. We have validated the proposed algorithm on 100 sub images(40 spiculated and 60 non spiculated) and results shows 90.5% of sensitivity with 0.87 Area Under Curve. Proposed techniques were compared and contrasted with the existing methods and result demonstrates that proposed algorithm has successfully detected spiculated mass ROI candidates in breast ultrasound images.
A Hybrid Image Filtering Method for Computer-Aided Detection of Microcalcification Clusters in Mammograms  [PDF]
Xiaoyong Zhang,Noriyasu Homma,Shotaro Goto,Yosuke Kawasumi,Tadashi Ishibashi,Makoto Abe,Norihiro Sugita,Makoto Yoshizawa
Journal of Medical Engineering , 2013, DOI: 10.1155/2013/615254
Abstract: The presence of microcalcification clusters (MCs) in mammogram is a major indicator of breast cancer. Detection of an MC is one of the key issues for breast cancer control. In this paper, we present a highly accurate method based on a morphological image processing and wavelet transform technique to detect the MCs in mammograms. The microcalcifications are firstly enhanced by using multistructure elements morphological processing. Then, the candidates of microcalcifications are refined by a multilevel wavelet reconstruction approach. Finally, MCs are detected based on their distributions feature. Experiments are performed on 138 clinical mammograms. The proposed method is capable of detecting 92.9% of true microcalcification clusters with an average of 0.08 false microcalcification clusters detected per image. 1. Introduction Breast cancer is one of the major causes of mortality in middle-aged women, especially in developed countries [1]. At present, there are no effective ways to prevent breast cancer since its cause remains unknown [2]. Therefore, early detection becomes the key to improving the breast cancer prognosis and reducing the mortality rates. Mammography has been widely recognized as being one of the most effective imaging modalities for early detection of breast cancer. However, it is a hard work for radiologists to provide both accurate and uniform evaluation for the enormous number of mammograms generated in widespread screening. A computer-aided detection or diagnosis (CAD) system, which uses computer technologies to detect the typical signs of breast cancer, has been developed to provide a “second opinion” for radiologists and to improve the accuracy and stability of diagnosis. In general, there are three signs of breast cancer in a mammogram: microcalcification clusters (MCs), architectural distortions, and masses [2]. In this paper, we particularly focus on the detection of MCs since they appear in 30–50% of mammographic diagnosed cases and show a high correlation with breast cancer [3]. According to the Breast Image Reporting and Data System (BI-RADS) lexicon [4], MCs are tiny calcium deposits that appear as small bright spots in mammograms. As an example, Figure 1 shows an MC in a mediolateral-oblique (MLO) mammogram. It is often hard for radiologists to find individual MCs in mammograms because they are very small (typically, 0.05–1?mm [3]) in the size and the contrast between the MCs and the surrounding breast tissue is not high enough. Figure 1: An example of an MC. (a) A mediolateral-oblique (MLO) mammogram. (b) Expanded view
A comparative evaluation of two algorithms of detection of masses on mammograms  [PDF]
Guillaume Kom,Alain Tiedeu,Martin Kom,John Ngundam
Computer Science , 2012, DOI: 10.5121/sipij.2012.3102
Abstract: In this paper, we implement and carry out the comparison of two methods of computer-aided-detection of masses on mammograms. The two algorithms basically consist of 3 steps each: segmentation, binarization and noise suppression using different techniques for each step. A database of 60 images was used to compare the performance of the two algorithms in terms of general detection efficiency, conservation of size and shape of detected masses.
A Comparative Evaluation of Two Algorithms of Detection of Masses on Mammograms  [PDF]
Guillaume Kom,Alain Tiedeu,Martin Kom,John Ngundam
Signal & Image Processing , 2012,
Abstract: In this paper, we implement and carry out the comparison of two methods of computer-aided-detection ofmasses on mammograms. The two algorithms basically consist of 3 steps each: segmentation, binarizationand noise suppression using different techniques for each step. A database of 60 images was used to compare the performance of the two algorithms in terms of general detection efficiency, conservation of size and shape of detected masses.
Detection of Masses from Mammograms Using Mass shape Pattern  [PDF]
Aswini Kumar Mohanty,Mrs. Arati Pradhan,Mrs. Swasati Sahoo,Saroj Kumar Lenka
International Journal of Computer Technology and Applications , 2011,
Abstract: The purpose of this study was to develop a new method for automated mass detection in digital mammographic images using mass shape pattern. Masses were detected using a two steps process. First, the pixels in the mammogram images were scanned in 8 directions, and regions of interest (ROI) were identified using various thresholds. Then, a mass shape pattern was used to categorize the ROI as true masses or non-masses based on their morphologies. Each pixel of a ROI was scanned with a mass shape pattern to determine whether there was a shape (part of a ROI) similar to the mass in the shape pattern. The similarity was controlled using two thresholds. If a shape was detected, then the coordinates of the shape were recorded as part of a true mass.To test the system’s efficiency, we applied this process to 52 mammogram images from the Mammographic Image Analysis Society (MIAS) database. Three hundred and thirty-two ROI were identified using the ROI specification methods. These ROI were classified using three mass shape pattern whose diameters were 10, 20 and 30 pixels. The results of this experiment showed that using the mass shape pattern with these diameters achieved sensitivities of 93%, 90% and 81% with 1.3, 0.7 and 0.33 false positives per image respectively. These results indicate that the detection performance of this shape pattern based algorithm is satisfactory, and may improve the performance of computer-aided analysis of mammographic images and early diagnosis of mammographic masses.
Segmentation of Breast Masses in Digital Mammograms Using Adaptive Median Filtering and Texture Analysis  [PDF]
Dr. Naseer M. Basheer,Mr. Mustafa H. Mohammed
International Journal of Recent Technology and Engineering , 2013,
Abstract: Breast cancer continues to be one of the major causes of death among women. Early detection is a key factor to the success of treatment process. X-ray mammography is one of the most common procedures for diagnosing breast cancer due to its simplicity, portability and cost effectiveness. Mass detection using Computer Aided Diagnosis (CAD) schemes was an active field of research in the past few years, and some of these studies showed a promising future. T`hese CAD systems serve as a second decision tool to radiologists for discovering masses in the mammograms. In this paper, a breast mass segmentation method is presented based on adaptive median filtering and texture analysis. The algorithm is implemented using MATLAB environment. The program accepts a digital mammographic image (images taken from the Mammographic Image Analysis Society (MIAS) database). Adaptive median filtering is applied for contouring the image, then the best contour is chosen based on the texture properties of the resulting Region-of-Interest (ROI). The proposed CAD system produces (92.307%) mass sensitivity at 2.75 False Positive per Image (FPI) which is considered as a proper result in this field of research.
An Improved Modified Tracking Algorithm Hybrid with Fuzzy C Means Clustering In Digital Mammograms  [PDF]
R.Sivakumar,Marcus Karnan,G.Gokila Deepa
International Journal of Computer Technology and Applications , 2012,
Abstract: Breast cancer is one of the major causes for the increase in mortality among women, especially in developed countries. Micro calcifications in breast issue is one of the most incident signs considered by radiologist for an early diagnosis of breast cancer, which is one of the most common forms of cancer among women. Mammography has been shown to be the most effective and reliable method for early signs of breast cancer such as masses, calcifications, bilateral asymmetry and architectural distortion. In this paper the thresholding algorithm is applied for the breast boundary identification and a modified tracking algorithm is introduced for pectoral muscle determination in Mammograms. Fuzzy C-means Clustering algorithm (FCM) is a method that is frequently used for image segmentation purpose. It has the advantage of giving good modeling results in many cases, although, it is not capable of specifying the number of clusters by itself
Mammograms in cosmetic breast surgery  [cached]
Shiffman M
Indian Journal of Plastic Surgery , 2005,
Abstract: Mammograms are necessary as preoperative preparation for breast surgery in certain patients. There is a definite need for mammograms preoperatively in patients over 40 who should be obtaining mammograms on an annual basis. Under the age of 40 mammograms are up to the discretion of the surgeon. Postoperative mammograms should be obtained as a baseline 6-12 months after breast surgery.
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