Improvement of Automated Detection Method for Clustered Microcalcification Based on Wavelet Transformation and Support Vector Machine

Arai, Kohei; Abdullah, Indra Nugraha; Okumura, Hiroshi; Kawakami, Rie

doi:10.14569/ijarai.2013.020405

Cited by 10 publications

(6 citation statements)

References 8 publications

(7 reference statements)

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“…Selected Features were then fed to linear SVM classifier. Compared to the results reported by Zhang et al [21], Arai et al [22] and Simon et al [23], the proposed system achieved the highest performance measures, as seen in table 1. However, Gurcan et al [10] outperformed all studies in comparison.…”

Section: Resultsmentioning

confidence: 53%

“…To the latest of our knowledge, Zhang et al [21] and Simon et al [23] used MIAS database which contains only 25 images with microcalcifications while Arai et al [22] employed dataset from Japanese Society of Medical Imaging Technology (JAMIT) comprising 12 microcalcifications images. In addition, Gurcan et al [10] used Nijmegen database with 40 microcalification images Compared to the dataset used in our study, the comparison is still fair as the proposed system can be considered as more robust algorithm.…”

Section: Resultsmentioning

confidence: 99%

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Spectral correlation analysis for microcalcification detection in digital mammogram images

Khalaf

Yassine

2015

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)

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Breast cancer has been threatening lives of women around the world. Thus, Computer Aided Diagnosis (CAD) systems play an important role in early detection of breast cancer. In this study, we propose a CAD system based on cyclostationary signal analysis for microcalcifications detection. Spectral correlation is estimated for regions of interests (ROIs) after conversion to 1D vector. The proposed algorithm utilizes simple statistical features calculation for the raw spectral data followed by student-t test for evaluation and reduction of the generated set of features. Support Vector Machines (SVM) with linear kernel was employed for the classification task. The experimental results showed that the proposed approach is superior compared to several state of the art methods. This approach was tested using digital database for screening mammogram (DDSM) and it achieved sensitivity, specificity and accuracy of 95.88%, 93.10% and 94.44% respectively.

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Section: Resultsmentioning

confidence: 53%

Section: Resultsmentioning

confidence: 99%

Spectral correlation analysis for microcalcification detection in digital mammogram images

Khalaf

Yassine

2015

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)

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“…Arai et al [4] separated the database taken from Japanese Society of Computer Aided Medical Imaging Technology into two parts, training and testing with the data proportion were 74% and 26%, respectively. The author used the features that are mostly statistical including mean, variance, max, coefficient of variation, standard deviation, and two additional features, 7 Hu moments and centroid.…”

Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Computer-Aided Detection System based on Simple Statistical Features and SVM Classification

Osman¹,

Alqasemi²

2020

IJACSA

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Computer-Aided Detection (CADe) systems are becoming very helpful and useful in supporting physicians for early detection of breast cancer. In this paper, a CADe system that is able to detect abnormal clusters in mammographic images will be implemented using different classifiers and features. The CADe system will utilize a Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) as classifiers. Adopting mammographic database from Mammographic Image Analysis Society (MIAS), for training and testing, the performance of the two types of classifiers are compared in terms of sensitivity, specificity, and accuracy. The obtained values for the previous parameters show the efficiency of the CADe system to be used as a secondary screening method in detecting abnormal clusters given the Region of Interest (ROI). The best classifier is found to be SVM showed 96% accuracy, 92% sensitivity and 100% specificity.

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“…Various image processing techniques are useful for the detection of microcalcification [6,7] and classification of cancer as benign and malignant using textural and shape features. A wide literature survey reveals that the conventional approach for detecting and classifying microcalcification involves enhancement [8,9], followed by segmentation [10,11], feature extraction [12], and classification [13]. In general, X-ray mammograms [14], sonograms [15], computed tomography [16], and magnetic resonance imaging [17] are the tools used for detecting breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition and Size Prediction of Microcalcification Based on Physical Characteristics by Using Digital Mammogram Images

et al. 2018

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Breast cancer is one of the life-threatening cancers occurring in women. In recent years, from the surveys provided by various medical organizations, it has become clear that the mortality rate of females is increasing owing to the late detection of breast cancer. Therefore, an automated algorithm is needed to identify the early occurrence of microcalcification, which would assist radiologists and physicians in reducing the false predictions via image processing techniques. In this work, we propose a new algorithm to detect the pattern of a microcalcification by calculating its physical characteristics. The considered physical characteristics are the reflection coefficient and mass density of the binned digital mammogram image. The calculation of physical characteristics doubly confirms the presence of malignant microcalcification. Subsequently, by interpolating the physical characteristics via thresholding and mapping techniques, a three-dimensional (3D) projection of the region of interest (RoI) is obtained in terms of the distance in millimeter. The size of a microcalcification is determined using this 3D-projected view. This algorithm is verified with 100 abnormal mammogram images showing microcalcification and 10 normal mammogram images. In addition to the size calculation, the proposed algorithm acts as a good classifier that is used to classify the considered input image as normal or abnormal with the help of only two physical characteristics. This proposed algorithm exhibits a classification accuracy of 99%.

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Improvement of Automated Detection Method for Clustered Microcalcification Based on Wavelet Transformation and Support Vector Machine

Cited by 10 publications

References 8 publications

Spectral correlation analysis for microcalcification detection in digital mammogram images

Spectral correlation analysis for microcalcification detection in digital mammogram images

Breast Cancer Computer-Aided Detection System based on Simple Statistical Features and SVM Classification

Pattern Recognition and Size Prediction of Microcalcification Based on Physical Characteristics by Using Digital Mammogram Images

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