A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories

Vadivel, A.; Surendiran, B.

doi:10.1016/j.compbiomed.2013.01.004

Cited by 61 publications

(33 citation statements)

References 28 publications

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“…Results are compared to these reported in [10,11]. The proposed algorithm used less number of features for the first classification step.…”

Section: Experiments and Resultsmentioning

confidence: 98%

“…The algorithm is tested using 224 mammogram masses from DDSM database. They achieved accuracy of 87.76% in differentiating the four mass shapes, 100% in differentiating between round and oval masses, and accuracy 93.29% in differentiating between lobular and irregular masses [11].…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the shape of the masses, Vadivel and Surendiran [10,11] developed an algorithm for classifying masses into the four basic shapes; round, oval, lobular, and irregular. Shape features have been extracted and then the feature vector is applied to the C5.0 decision tree algorithm and fuzzy inference systems.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, many methods have been developed for the detection and classification of masses or microcalcifications [5][6][7][8][9][10][11][12][13]. For the detection of masses, the Growing Neural Gas (GNG) has been used with the Supported Vector Machine (SVM) [5].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Segmentation and Classification of Masses from Digital Mammograms

Mohamed

Salem

Hadhoud

et al. 2016

AIVP

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Breast cancer is one of the leading causes of death among female cancer patients. Mammography is the most efficient method for the early detection of abnormalities that are associated with breast cancer. Masses and micro calcifications are the most popular abnormalities that indicate breast cancer. The proposed paper intends to develop an automated system for assisting the analysis of digital mammograms. First, a preprocessing step is applied to enhance images followed by a segmentation step that is based on morphological operations and Otsu's thresholding techniques. Thereafter, shape features are extracted from the segmented region and used in the classification process. Finally, the classification step to classify the segmented shape as round, oval, lobular, or irregular. The algorithm is tested using 270 mammogram images from the Women Health Care Program (WHC) and 142 publicly available images from the Digital Database for Screening Mammography (DDSM). Results show that the proposed technique effectively detects and segments masses from mammogram images. The shape of segmented masses is classified into either round, oval, lobular, or irregular. Round and oval shapes are classified with 100% accuracy while lobular and irregular shapes results in accuracy of 93% using the ANN for the WHC dataset and 100%. On the other hand, accuracy for images from the DDSM is 100% and 91.3% respectively.

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“…Results are compared to these reported in [10,11]. The proposed algorithm used less number of features for the first classification step.…”

Section: Experiments and Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Segmentation and Classification of Masses from Digital Mammograms

Mohamed

Salem

Hadhoud

et al. 2016

AIVP

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“…Generally, previous researches, for the CADe system, use the mammography that is developed based on the three features on the mammogram image, those are the features of color, texture and shape. [8] use the color feature, while [9] use the shape feature and [6] combine the shape and texture on their research. Among both features, the last one is most widely used for mammography in previous researches [10] [11][12] [13] and [14].…”

Section: Introductionmentioning

confidence: 99%

Improvement of Sample Selection: A Cascade-Based Approach for Lesion Automatic Detection

Uyun¹,

Wahyudi²,

Choridah³

2016

ijacsa

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Abstract-Computer-Aided Detection (CADe) system has a significant role as a preventative effort in the early detection of breast cancer. There are some phases in developing the pattern recognition on the CADe system, including the availability of a large number of data, feature extraction, selection and use of features, and the selection of the appropriate classification method. Haar cascade classifier has been successfully developed to detect the faces in the multimedia image automatically and quickly. The success of the face detection system must not be separated from the availability of the training data in the large numbers. However, it is not easy to implement on a medical image because of some reasons, including its low quality, the very little gray-value differences, and the limited number of the patches for the examples of the positive data. Therefore, this research proposes an algorithm to overcome the limitation of the number of patches on the region of interest to detect whether the lesion exists or not on the mammogram images based on the Haar cascade classifier. This research uses the mammogram and ultrasonography images from the breast imaging of 60 probands and patients in the Clinic of Oncology, Yogyakarta. The testing of the CADe system is done by comparing the reading result of that system with the mammography reading result validated with the reading of the ultrasonography image by the Radiologist. The testing result of the k-fold cross validation demonstrates that the use of the algorithm for the multiplication of intersection rectangle may improve the system performance with accuracy, sensitivity, and specificity of 76%, 89%, and 63%, respectively.

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