A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs

Rangayyan, Rangaraj M.; Ayres, Fábio J.; Desautels, J. E. Leo

doi:10.1016/j.jfranklin.2006.09.003

Cited by 302 publications

(159 citation statements)

References 109 publications

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“…It has been shown that the performance of radiologists can be increased by providing them the results of a CAD system [3,39]. Mammogram-based CAD systems were designed with the aim of increasing the effectiveness and efficiency of screening procedures.…”

Section: Literature Surveymentioning

confidence: 99%

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Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM

2014

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This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant. Original mammogram is preprocessed to separate the breast region from its background. To work on the suspicious area of the breast, region of interest (ROI) patches of a fixed size of 128 × 128 are extracted from the original large-sized digital mammograms. For training, patches are extracted manually from a preprocessed mammogram. For testing, patches are extracted from a highly dense area identified by clustering technique. For all extracted patches corresponding to a mammogram, Zernike moments of different orders are computed and stored as a feature vector. A support vector machine (SVM) is used to classify extracted ROI patches. The experimental study shows that the use of Zernike moments with order 20 and SVM classifier gives better results among other studies. The proposed system is tested on Image Retrieval In Medical Application (IRMA) reference dataset and Digital Database for Screening Mammography (DDSM) mammogram database. On IRMA reference dataset, it attains 99 % sensitivity and 99 % specificity, and on DDSM mammogram database, it obtained 97 % sensitivity and 96 % specificity. To verify the applicability of Zernike moments as a fitting texture descriptor, the performance of the proposed CAD system is compared with the S. Sharma · P. Khanna ( ) Pandit Dwarka Prasad Mishra Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Dumna Airport Road, P.O.: Khamaria, Jabalpur, Madhya Pradesh 482 005, India e-mail: pkhanna@iiitdmj.ac.in S. Sharma e-mail: shubhi.sharma@iiitdmj.ac.in other well-known texture descriptors namely gray-level cooccurrence matrix (GLCM) and discrete cosine transform (DCT).

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Section: Literature Surveymentioning

confidence: 99%

“…Fu et al [17] proposed a two-stage procedure to detect microcalcifications. Rangayyan et al [39] gave an overview of techniques for detection and analysis of calcifications, masses, tumors, bilateral asymmetry, and architectural distortion. Xu et al [50] gave a new algorithm based on two artificial neural networks (ANNs).…”

Section: Literature Surveymentioning

confidence: 99%

Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM

2014

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“…Then we reconstructed two image, compared with even image and odd image, show Fig. 4 (a), (b) and (c), (d) show the image of original and (2) reconstructed. Then we know even the change is similar but it still made our feature more batter than original image.…”

Section: Feature Extractionmentioning

confidence: 99%

“…There are two system are developed to assist doctor. One is called computer aided diagnosis and the other is called computer aided detection [2]. Concerning image segmentation and specification of regions of interest (ROIs), several methods have been proposed such as classical image filtering and local thresholding [3], techniques based on mathematical morphology [4], wavelet analysis [5] and multiscale analysis based on a specialized Gaussian and Peitgen [6].…”

Section: Introductionmentioning

confidence: 99%

The Classification of Breast Calcification using Texture Feature of Fractal-based and Gabor Wavelet-based

Po-Tsun

Chin²,

Jing-Shian³

et al. 2013

Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)

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According to the opinions of specialized doctors, being able to accurately classify breast calcification is very important, and with this information available, only then medical treatments can be applied properly. However, for any delay treatment or misdiagnosis, it is very likely as the key attributed to the fatal death of the patients. Currently, there are a lot of researches on the development of many methods with application of mammographic for classification of breast calcification out there already. However, in this paper, mammography image is used to classify breast calcification. And, we use a cubic curve contrast enhancement method to enhance image contrast. Next, we use Gabor wavelet and Fractal-based to extract texture feature on the breast image. Finally, we further to input these features into Back-propagation neural network method for classification. Next, we will make classification of these different features, as well as to put a link among them, in order to get better accuracy for classification. Experimental result shows our method has a good accuracy, and be able to precisely help the doctors for recognizing the breast calcification whether they are good or not. In addition, it can be verified that the accuracy rate of our method is up to 89.55%.

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“…However, radiologists would like the ability of CAD to change the contrast of a mammogram [13]. In this situation, we would like to present a simple model to assist radiologists for their diagnosis without having to use computer.…”

Section: Introductionmentioning

confidence: 99%

Presenting a Simplified Assistant Tool for Breast Cancer Diagnosis in Mammography to Radiologists

Zhang

Doust

Kumar

2010

Lecture Notes in Computer Science

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Abstract. This paper proposes a method to simplify a computational model from logistic regression for clinical use without computer. The model was built using human interpreted featrues including some BI-RADS standardized features for diagnosing the malignant masses. It was compared with the diagnosis using only assessment categorization from BI-RADS. The research aims at assisting radiologists to diagnose the malignancy of breast cancer in a way without using automated computer aided diagnosis system.

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A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs

Cited by 302 publications

References 109 publications

Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM

Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM

The Classification of Breast Calcification using Texture Feature of Fractal-based and Gabor Wavelet-based

Presenting a Simplified Assistant Tool for Breast Cancer Diagnosis in Mammography to Radiologists

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