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Proceedings ArticleDOI: 10.1109/ICCPCT.2015.7159454

Identification of abnormility from digital mammogram to detect breast cancer

19 Mar 2015-pp 1-5
Abstract: The breast cancer is diagnosed using many ways for past two decades. The Studies have proved that the early detection of cancer will increase the life span of the patients. The breast cancer detection requires double reading of mammogram by radiologist, hence the radiologist need to have support from CAD which includes different image processing techniques. We are in urge to improve the CAD systems that detects the abnormalities such as micro calcification, mass, etc. than existing. Firstly, This paper focus on the preprocessing which removes noise from the mammogram and it is followed by segmentation of the image which helps to partition the image and to identify the abnormalities which could cause cancer. The segmentation is made by OTSU's method which helps us further to classify the abnormalities from the normal.

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Topics: Breast cancer (52%)
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Proceedings ArticleDOI: 10.1109/IEMENTECH.2017.8076982
R. D. Ghongade1, D. G. WakdeInstitutions (1)
01 Apr 2017-
Abstract: Neural Network is utilized as a developing analytic tool for the diagnosis of breast cancer. The goal of this research is to determine breast tumor from digital mammograms with a machine learning technique in view of RF and combination of RF-ELM classifier. For digital mammogram images, MIAS database is used. Preprocessing is usually needed to enhance the low quality of the image. The region of interest (ROI) is determined in line with the scale of suspicious region. After the suspicious area is sectioned, features are extracted by texture analysis. GLCM is used as a texture attribute to extract the suspicious area. From all extracted features best features are selected with the help of CBF method. To enhance the exactness of classification, only six features are selected. These features are mean, standard deviation, kurtosis, variance, entropy and correlation coefficient. RF and RF-ELM are used as a classifier. The outcomes of present work show that the CAD system with the usage of RF-ELM classifier may be very powerful and achieves the exceptional results in the prognosis of breast cancer.

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Topics: Region of interest (51%), Mammography (50%), Image segmentation (50%)

13 Citations


Book ChapterDOI: 10.1007/978-981-13-0866-6_8
01 Jan 2019-
Abstract: Since last 60 years, bosom (breast) tumor is the major cause of death amid females worldwide. Earliest possible detection will raise the endurance rate of patients. Premature detection of bosom tumor is big challenge in medical science. Medical studies proven that imaging modalities like mammography, thermography, ultrasound, and magnetic resonance imaging (MRI) play a vigorous role to detect breast irregularity earliest. This paper enhances the knowledge on two imaging practices, one is mammography and another is thermography. It aids to identify the limitations in existing technologies and helps to plan the new methodology.

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Topics: Mammography (56%), Magnetic resonance imaging (51%)

1 Citations


Book ChapterDOI: 10.1007/978-981-15-0994-0_7
01 Jan 2020-
Abstract: Breast cancer is the most prevailing type of cancer responsible for a large number of deaths every year. However, at the same time, this is largely a curable type of cancer if identified at initial stages. With major advances in research in the areas of image processing, data mining and clustering and machine learning, a more precise prognosis and prediction of breast cancer are possible at earlier stages. A fuzzy clustering model is a popular model used across various researches in image processing to predict the malignancy of breast tumor. The partitional clustering method finds its strength in its fuzzy partitioning such that a data point may belong to different classes with varying degrees of membership (ranging between 0 and 1), which is less rigid as compared to an older and still popular k-means clustering algorithm. The current article attempts to hybridize the fuzzy C-means with the cohort intelligence (CI) algorithm to optimize cluster formation. CI is a robust optimization metaheuristic belonging to the class of socio-inspired optimizers (Kumar M, Kulkarni A Socio-cultural inspired metaheuristics, pp 1–28, Springer International Publishing, 2019 [22]), motivated from self-adapting behavior of candidates in a cohort or a group. CI is typically characterized by its simple algorithmic nature, robust structure and a faster convergence rate, hence gaining popularity. This novel hybridized data clustering algorithm fuzzy-CI imitates the soft clustering and communal learning attitude of clusters and candidates. The hybridized method of fuzzy-CI is validated by testing it on the Breast Cancer Wisconsin (Diagnostic) Dataset. The results validate that the hybridized version exhibits better cluster formation in comparison with the non-hybridized version.

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Topics: Fuzzy clustering (68%), Cluster analysis (63%)

1 Citations

References
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Open access
01 Jan 1994-

899 Citations


Journal ArticleDOI: 10.1016/J.MEDIA.2009.12.005
Arnau Oliver1, Jordi Freixenet1, Joan Martí1, Elsa Pérez  +3 moreInstitutions (3)
Abstract: The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approaches. In contrast with other reviews which only describe and compare different approaches qualitatively, this review also provides a quantitative comparison. The performance of seven mass detection methods is compared using two different mammographic databases: a public digitised database and a local full-field digital database. The results are given in terms of Receiver Operating Characteristic (ROC) and Free-response Receiver Operating Characteristic (FROC) analysis.

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352 Citations


Journal ArticleDOI: 10.1148/RADIOLOGY.191.2.8153302
W P Kegelmeyer1, J M Pruneda, P D Bourland, A Hillis  +2 moreInstitutions (1)
01 May 1994-Radiology
Abstract: PURPOSE: To study the use of a computer vision method as a second reader for the detection of spiculated lesions on screening mammograms. MATERIALS AND METHODS: An algorithmic computer process for the detection of spiculated lesions on digitized screen-film mammograms was applied to 85 four-view clinical cases: 36 cases with cancer proved by means of biopsy and 49 cases with negative findings at examination and follow-up. The computer detections were printed as film with added outlines that indicated the suspected cancers. Four radiologists screened the 85 cases twice, once without and once with the computer reports as ancillary films. RESULTS: The algorithm alone achieved 100% sensitivity, with a specificity of 82%. The computer reports increased the average radiologist sensitivity by 9.7% (P = .005), moving from 80.6% to 90.3%, with no decrease in average specificity. CONCLUSION: The study demonstrated that computer analysis of mammograms can provide a substantial and statistically significant increase ...

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337 Citations


Journal ArticleDOI: 10.1109/42.57760
D. Brzakovic1, X.M. Luo2, P. BrzakovicInstitutions (2)
Abstract: An automated system for detecting and classifying particular types of tumors in digitized mammograms is described The analysis of mammograms is performed in two stages First, the system identifies pixel groupings that may correspond to tumors Next, detected pixel groupings are subjected to classification The essence of the first processing stage is multiresolution image processing based on fuzzy pyramid linking The second stage uses a classification hierarchy to identify benign and malignant tumors Each level of the hierarchy uses deterministic or Bayes classifiers and a particular measurement The measurements pertain to shape and intensity characteristics of particular types of tumors The classification hierarchy is organized in such a way that the simplest measurements are used at the top, with the system stepping through the hierarchy only when it cannot classify the detected pixel groupings with certainty >

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296 Citations


Proceedings ArticleDOI: 10.1109/IECBES.2010.5742205
01 Nov 2010-
Abstract: Mammography is currently the most effective imaging modality used by radiologists for the screening of breast cancer Finding an accurate, robust and efficient breast profile segmentation technique still remains a challenging problem in digital mammography Extraction of the breast profile region and the pectoral muscle is an essential pre-processing step in the process of computer-aided detection Primarily it allows the search for abnormalities to be limited to the region of the breast tissue without undue influence from the background of the mammogram The presence of pectoral muscle in mammograms biases detection procedures, which recommends removing the pectoral muscle during mammogram pre-processing In this paper we explore an automated technique for mammogram segmentation The proposed algorithm uses morphological preprocessing and seeded region growing (SRG) algorithm in order to: (1) remove digitization noises, (2) suppress radiopaque artifacts, (3) separate background region from the breast profile region, and (4) remove the pectoral muscle, for accentuating the breast profile region To demonstrate the capability of our proposed approach, digital mammograms from two separate sources are tested using Ground Truth (GT) images for evaluation of performance characteristics Experimental results obtained indicate that the breast regions extracted accurately correspond to the respective GT images

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Topics: Digital mammography (62%), Mammography (57%), Region growing (53%) ...read more

110 Citations


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