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

Silva, Joberth de Nazaré; Filho, Antônio Oséas de Carvalho; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso de; Gattass, Marcelo

doi:10.1007/s10278-014-9739-3

Cited by 30 publications

(11 citation statements)

References 31 publications

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“…The proposed approach could get better results as compared to standard SVM. A team of researcher presented a method in [110] for detecting masses from mammograms. Thresholding, correlation function and SVM tackled the detection process.…”

Section: Svm For Mammogram Analysismentioning

confidence: 99%

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

et al. 2021

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Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper’s main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.

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Section: Svm For Mammogram Analysismentioning

confidence: 99%

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

et al. 2021

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“…SVM has a prominent advantage in theoretical research in recent years. SVM classifier is widely used for breast cancer identificatin, ROI segmentation, feature extraction [30], [31], [46], [58], [59], [68], [75], [85]- [88]. In this study, 63 publications used SVM classifiers for breast cancer diagnoses that are 27% of the selected studies.…”

Section: Machine Learning Models For Breast Lesions Diagnosticsmentioning

confidence: 99%

“…R. Rasti et al [29] developed a robust DL model for ROI segmentation and breast tumor classification using segmented DCE-MRI images. J. de Nazar et al [30] proposed a model by selecting the variable value of the threshold for the segmentation of breast masses. J. Y. Choi et al [31] designed a CAD model to extracts the ROI before the breast cancer classification.…”

Section: Introductionmentioning

confidence: 99%

A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities

Mahmood

Pei

et al. 2020

IEEE Access

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Patients with breast cancer are prone to serious health-related complications with higher mortality. The primary reason might be a misinterpretation of radiologists in recognizing suspicious lesions due to technical issues in imaging qualities and heterogeneous breast densities which increases the false-(positive and negative) ratio. Early intervention is significant in establishing an up-to-date prognosis process which can successfully mitigate complications of disease with higher recovery. The manual screening of breast abnormalities through traditional machine learning schemes misinterpret the inconsistent featureextraction process which poses a problem, i.e., patients being called-back for biopsies to eliminates the suspicions. However, several deep learning-based methods have been developed for reliable breast cancer prognosis and classification but very few of them provided a comprehensive overview of lesions segmentation. This research focusses on providing benefits and risks of breast multi-imaging modalities, segmentation schemes, feature extraction, classification of breast abnormalities through state-of-the-art deep learning approaches. This research also explores various well-known databases using "Breast Cancer" keyword to present a comprehensive survey on existing diagnostic schemes to open-up new research challenges for radiologists and researchers to intervene as early as possible to develop an efficient and reliable breast cancer prognosis system using prominent deep learning schemes.

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“…Hence, preprocessing is necessary and should be the first of the four stages in a CAD system. Some studies have tried to overcome the problem of low contrast using histogram processing operations [ 8 – 11 ], morphological operations [ 12 ], and statistics theory [ 13 ], while unsharp filtering [ 8 ], wavelet transform [ 12 , 13 ], and median filtering [ 14 , 15 ] are the most common noise reduction.…”

Section: Introductionmentioning

confidence: 99%

A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

Esener

Ergin

Yüksel

2017

Journal of Healthcare Engineering

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A new and effective feature ensemble with a multistage classification is proposed to be implemented in a computer-aided diagnosis (CAD) system for breast cancer diagnosis. A publicly available mammogram image dataset collected during the Image Retrieval in Medical Applications (IRMA) project is utilized to verify the suggested feature ensemble and multistage classification. In achieving the CAD system, feature extraction is performed on the mammogram region of interest (ROI) images which are preprocessed by applying a histogram equalization followed by a nonlocal means filtering. The proposed feature ensemble is formed by concatenating the local configuration pattern-based, statistical, and frequency domain features. The classification process of these features is implemented in three cases: a one-stage study, a two-stage study, and a three-stage study. Eight well-known classifiers are used in all cases of this multistage classification scheme. Additionally, the results of the classifiers that provide the top three performances are combined via a majority voting technique to improve the recognition accuracy on both two- and three-stage studies. A maximum of 85.47%, 88.79%, and 93.52% classification accuracies are attained by the one-, two-, and three-stage studies, respectively. The proposed multistage classification scheme is more effective than the single-stage classification for breast cancer diagnosis.

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

Cited by 30 publications

References 31 publications

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities

A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

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