Threshold Based Segmentation Technique for Mass Detection in Mammography

Makandar, Aziz; Halalli, Bhagirathi

doi:10.17706/jcp.11.6.463-4712

Cited by 31 publications

(12 citation statements)

References 7 publications

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“…Thresholding is yet effective and simple method of segmenting the image into different regions. In proposed algorithm, before applying thresholding to the image it transformed with watershed and morphological operations [29]. Watershed was originally proposed by Digabel and Lantuejoul [30,31].…”

Section: Adaptive Thresholding Segmentation Techniquesmentioning

confidence: 99%

Computer Aided Diagnosis - Medical Image Analysis Techniques

Halalli¹,

Makandar²

2018

Breast Imaging

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Breast cancer is the second leading cause of death among women worldwide. Mammography is the basic tool available for screening to find the abnormality at the earliest. It is shown to be effective in reducing mortality rates caused by breast cancer. Mammograms produced by low radiation X-ray are difficult to interpret, especially in screening context. The sensitivity of screening depends on image quality and unclear evidence available in the image. The radiologists find it difficult to interpret the digital mammography; hence, computer-aided diagnosis (CAD) technology helps to improve the performance of radiologists by increasing sensitivity rate in a cost-effective way. Current research is focused toward the designing and development of medical imaging and analysis system by using digital image processing tools and the techniques of artificial intelligence, which can detect the abnormality features, classify them, and provide visual proofs to the radiologists. The computer-based techniques are more suitable for detection of mass in mammography, feature extraction, and classification. The proposed CAD system addresses the several steps such as preprocessing, segmentation, feature extraction, and classification. Though commercial CAD systems are available, identification of subtle signs for breast cancer detection and classification remains difficult. The proposed system presents some advanced techniques in medical imaging to overcome these difficulties.

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Section: Adaptive Thresholding Segmentation Techniquesmentioning

confidence: 99%

Computer Aided Diagnosis - Medical Image Analysis Techniques

Halalli¹,

Makandar²

2018

Breast Imaging

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“…The threshold segmentation method for the detection of masses in mammography was proposed in [105]. This method detects a region of mass using a morphological threshold.…”

Section: Mammograms Segmentation Based On Thresholdmentioning

confidence: 99%

Breast Cancer Segmentation Methods: Current Status and Future Potentials

Michael

et al. 2021

BioMed Research International

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Early breast cancer detection is one of the most important issues that need to be addressed worldwide as it can help increase the survival rate of patients. Mammograms have been used to detect breast cancer in the early stages; if detected in the early stages, it can drastically reduce treatment costs. The detection of tumours in the breast depends on segmentation techniques. Segmentation plays a significant role in image analysis and includes detection, feature extraction, classification, and treatment. Segmentation helps physicians quantify the volume of tissue in the breast for treatment planning. In this work, we have grouped segmentation methods into three groups: classical segmentation that includes region-, threshold-, and edge-based segmentation; machine learning segmentation; and supervised and unsupervised and deep learning segmentation. The findings of our study revealed that region-based segmentation is frequently used for classical methods, and the most frequently used techniques are region growing. Further, a median filter is a robust tool for removing noise. Moreover, the MIAS database is frequently used in classical segmentation methods. Meanwhile, in machine learning segmentation, unsupervised machine learning methods are more frequently used, and U-Net is frequently used for mammogram image segmentation because it does not require many annotated images compared with other deep learning models. Furthermore, reviewed papers revealed that it is possible to train a deep learning model without performing any preprocessing or postprocessing and also showed that the U-Net model is frequently used for mammogram segmentation. The U-Net model is frequently used because it does not require many annotated images and also because of the presence of high-performance GPU computing, which makes it easy to train networks with more layers. Additionally, we identified mammograms and utilised widely used databases, wherein 3 and 28 are public and private databases, respectively.

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“…But, the boundary obtained was lower than the actual boundary and slower processing time Shi et al [14] Breast boundary segmentation and calcification detection Unsupervised pixel-wise labeling and texture filters The method motivated by achieving good accuracies. But, the segmentation errors could be further avoided Makandar et al [15] Segmentation of masses in breast region Adaptive thresholding method, watershed transformation and contour-based method The method promised high accuracy which could be further improved Rahmati et al [16] Segmentation of masses Maximum likelihood active contour method Showed robustness for choosing the seed point and possess high segmentation accuracy. The main problem of this method is that, it needs expert intervention and is time consuming Neto et al [17] Segmentation of masses Swarm optimization, area filters and texture descriptors Reduced false positives.…”

Section: Region-based Active Contour Modelmentioning

confidence: 99%

Breast Cancer Mass Detection in Mammograms Using Gray Difference Weight and MSER Detector

Divyashree

Kumar

2021

SN COMPUT. SCI.

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Breast cancer is a deadly and one of the most prevalent cancers in women across the globe. Mammography is widely used imaging modality for diagnosis and screening of breast cancer. Segmentation of breast region and mass detection are crucial steps in automatic breast cancer detection. Due to the non-uniform distribution of various tissues, it is a challenging task to analyze mammographic images with high accuracy. In this paper, background suppression and pectoral muscle removal are performed using gradient weight map followed by gray difference weight and fast marching method. Enhancement of breast region is performed using contrast limited adaptive histogram equalization (CLAHE) and de-correlation stretch. Detection of breast masses is accomplished by gray difference weight and maximally stable external regions (MSER) detector. Experimentation on Mammographic Image Analysis Society (MIAS) and curated breast imaging subset of digital database for screening mammography (CBIS-DDSM) show that the method proposed performs breast boundary segmentation and mass detection with best accuracies. Mass detection achieved high accuracies of about 97.64% and 94.66% for MIAS and CBIS-DDSM dataset, respectively. The method is simple, robust, less affected to noise, density, shape and size which could provide reasonable support for mammographic analysis.

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Threshold Based Segmentation Technique for Mass Detection in Mammography

Cited by 31 publications

References 7 publications

Computer Aided Diagnosis - Medical Image Analysis Techniques

Computer Aided Diagnosis - Medical Image Analysis Techniques

Breast Cancer Segmentation Methods: Current Status and Future Potentials

Breast Cancer Mass Detection in Mammograms Using Gray Difference Weight and MSER Detector

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