Mammogram classification using sparse-ROI: A novel representation to arbitrary shaped masses

Pavan, K. Karteeka; Chereddy, Srinivasa Rao

doi:10.1016/j.eswa.2016.03.037

Cited by 25 publications

(13 citation statements)

References 48 publications

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“…It can be observed from Figure 8 , curvelet subbands also provide shape of mass in 16 different directions so that the directional information can be associated with LBP features. Kanadam et al [ 3 ] used concept of sparse ROI; similarly, we have extended it for sparse curvelet subband and LBP features computation.…”

Section: Methodsmentioning

confidence: 99%

“…Several researchers have implemented clustering method like K-means and Fuzzy C-means (FCM) for breast abnormality segmentation [ 3 , 23 ]. However, they have limitations in terms of learning abilities.…”

Section: Literature Surveymentioning

confidence: 99%

“…This fact brings a concern and necessity that screening for breast cancer is prudent in its early stage to confirm longer survival. Among all techniques, namely, mammography, tomosynthesis, ultrasonography, computed tomography, and magnetic resonance, mammography is the most reliable and accepted modality by radiologist for preliminary examination of breast cancer due to cost benefits and accessibility [ 3 – 5 ]. The diagnosis of breast cancer using mammogram by radiologist varies from expert to expert as symptoms are misinterpreted or overlooked, due to the tedious task of screening mammograms.…”

Section: Introductionmentioning

confidence: 99%

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Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

Pawar¹,

Talbar

Dudhane

2018

Journal of Healthcare Engineering

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Breast Cancer is the most prevalent cancer among women across the globe. Automatic detection of breast cancer using Computer Aided Diagnosis (CAD) system suffers from false positives (FPs). Thus, reduction of FP is one of the challenging tasks to improve the performance of the diagnosis systems. In the present work, new FP reduction technique has been proposed for breast cancer diagnosis. It is based on appropriate integration of preprocessing, Self-organizing map (SOM) clustering, region of interest (ROI) extraction, and FP reduction. In preprocessing, contrast enhancement of mammograms has been achieved using Local Entropy Maximization algorithm. The unsupervised SOM clusters an image into number of segments to identify the cancerous region and extracts tumor regions (i.e., ROIs). However, it also detects some FPs which affects the efficiency of the algorithm. Therefore, to reduce the FPs, the output of the SOM is given to the FP reduction step which is aimed to classify the extracted ROIs into normal and abnormal class. FP reduction consists of feature mining from the ROIs using proposed local sparse curvelet coefficients followed by classification using artificial neural network (ANN). The performance of proposed algorithm has been validated using the local datasets as TMCH (Tata Memorial Cancer Hospital) and publicly available MIAS (Suckling et al., 1994) and DDSM (Heath et al., 2000) database. The proposed technique results in reduction of FPs from 0.85 to 0.02 FP/image for MIAS, 4.81 to 0.16 FP/image for DDSM, and 2.32 to 0.05 FP/image for TMCH reflecting huge improvement in classification of mammograms.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

Pawar¹,

Talbar

Dudhane

2018

Journal of Healthcare Engineering

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“…The median filtering technique is applied repetitively again and again if required edges present in the images are negligibly humiliate. In case of Mammogram images, some of the researchers [4,11] make use of a 2D median filtering approach to remove the unwanted straight lines present in the dominant part of mammogram images.…”

Section: A Median Filteringmentioning

confidence: 99%

Filtering Method for Pre processing Mammogram Images for Breast Cancer Detection

Ganvir*¹,

Yadav²

2019

IJEAT

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Breast cancer is a stand-out surrounded by the most widely perceived diseases and has a high rate of mortality around the world, significantly risking the health of the females. Among existing all modalities of medical scans, mammography is the most preferred modality for preliminary examination of breast cancer. To assist radiologist, a computer-aided diagnosis (CAD) is enhancing and important medical systems for mammographic lesion analysis. CAD is necessary to provide doctors, to improve detection quality of breast cancer. In mammogram images, micro-calcifications is one of the imperative sign for breast cancer detection. Mammographic medical scan may present unwanted noise and CAD systems are very sensitive to noise. So, pre-processing of medical images for any medical image analysis application like brain tumor detection, breast cancer detection, and interstitial lung disease classification is considered as an important step. The segmentation or classification accuracy is mainly depends upon the significant improved pre-processing process. Thus, in this work, different types of filtering techniques used for noise reduction in medical image processing are analyzed. The qualitative and quantitative results are examined on mini-MIAS mammogram image database. The effectiveness of filtering techniques is compared based on the different quantitative parameters and visual qualities of examined output.

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“…The procedure of identifying breast masses through the CAD system hold four paces. The initial one is pre-processing followed by segmentation, sparse_RIO, finally feature [12]. Noises and pectoral muscles are also visible in mammographic images, and it is difficult to identify lumps and pectoral muscles due to having identical pixel strength.…”

Section: Introductionmentioning

confidence: 99%

Imaging & Machine Learning Techniques Used for Early Identification of Cancer in Breast Mammogram

Tripathy¹,

Swarnkar²

2019

IJRTE

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 Abstract: Breast cancer has become a major concern of women health throughout the world and has an important cause of death among women. The important radiographic signs of cancer are the masses visible in the breast. In the initial stage, the masses in the women breast are very strenuous to detect. In many cases, it has been proven that a manual attempt of treatment methods are time consuming and inefficient. Hence there is a basic demand for well-planned methods for diagnosis of the cancerous cells with minimal human participation resulting high in precision. Mammography has been proven as an efficient technique for the identification of cancer in women breast. Automated detection of masses in breast mammogram is the major goal in the identification of cancer in women breast. Machine learning techniques can be used as an effective mechanism by the physician for the early detection of cancer in the breast. By early recognition of malignancy in the breast, patients will get treatment right from the initial stage of cancer which can save their lives. Keywords : breast cancer, computer aided detection (CAD), mammogram, machine learning (ML), region of interest (ROI) .

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Mammogram classification using sparse-ROI: A novel representation to arbitrary shaped masses

Cited by 25 publications

References 48 publications

Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

Filtering Method for Pre processing Mammogram Images for Breast Cancer Detection

Imaging & Machine Learning Techniques Used for Early Identification of Cancer in Breast Mammogram

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