A Benign and Malignant Mass Classification Algorithm Based on an Improved Level Set Segmentation and Texture Feature Analysis

Liu, Xiaoming; Liu, Jun; Zhou, Dongfeng; Tang, Jian

doi:10.1109/icbbe.2010.5518284

Cited by 23 publications

(3 citation statements)

References 13 publications

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“…On the other hand, features based on textural patterns of mammograms are widely used to distinguish between benign and malignant masses [11][12][13][14][15]. Liu et al [16] computed Haralick's features from Gray level co-occurrence matrix (GLCM), derived from the region around the contour of the mass, and achieved an area under the receiver operating characteristic (ROC) curve (A z ) of 0.70 for benign and malignant classification, respectively, with 309 DDSM images. Chakraborty et al [17,18] calculated Haralick's features from angle co-occurrence matrices (ACMs), extracted at different resolutions of three regions around masses, and obtained an A z of 0.86 and accuracy (A cc ) of 81.2% with 445 mass regions.…”

Section: Introductionmentioning

confidence: 99%

Analysis of 2D singularities for mammographic mass classification

Rabidas

Chakraborty

Midya

2016

IET Computer Vision

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Masses are one of the prevalent early signs of breast cancer, visible in mammogram. However, its variation in shape, size, and appearance often creates hazards in proper diagnosis of mammographic masses. This study analyses the 2D singularities of masses and their surrounding regions with Ripplet‐II transform to classify them as benign and malignant. Since benign and malignant masses may change the orientation patterns of normal breast tissues differently, several textural features including Ripplet‐II coefficients and statistical co‐variates, derived from the Ripplet‐II transformed images, are extracted to quantify the texture information of mammographic regions. The important features are then selected using stepwise logistic regression technique and evaluated using linear discriminant analysis and support vector machine with a ten‐fold cross‐validation. The best performance in terms of the area under the receiver operating characteristic curve of 0.91 ± 0.01 and 0.83 ± 0.01 and accuracy of 87.28 ± 0.02 and 75.60 ± 0.01 are obtained with the proposed method while experimenting with 58 images from the mini‐MIAS and 200 images from the Digital Database for Screening Mammography database, respectively.

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Section: Introductionmentioning

confidence: 99%

Analysis of 2D singularities for mammographic mass classification

Rabidas

Chakraborty

Midya

2016

IET Computer Vision

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“…GLCM has been widely used in mammographic microcalcifications [8] and masses [34]. We use several GLCM features, including autocorrelation (TF3), contrast (TF4), correlation (TF5), cluster prominence (TF6), cluster shade (TF7), energy (TF8), entropy (TF9), homogeneity (TF10), maximum probability (TF11), sum of squares (TF12), sum average (TF13), sum variance (TF14), sum entropy (TF15), difference variance (TF16), difference entropy (TF17), information measure of correlation (TF18, TF19), inverse difference normalized (TF20), and inverse difference moment normalized (TF21).…”

Section: Texture Featuresmentioning

confidence: 99%

Microcalcification detection in full-field digital mammograms with PFCM clustering and weighted SVM-based method

Liu

Mei

et al. 2015

EURASIP J. Adv. Signal Process.

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Clustered microcalcifications (MCs) in mammograms are an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we integrated the possibilistic fuzzy c-means (PFCM) clustering algorithm and weighted support vector machine (WSVM) for the detection of MC clusters in full-field digital mammograms (FFDM). For each image, suspicious MC regions are extracted with region growing and active contour segmentation. Then geometry and texture features are extracted for each suspicious MC, a mutual information-based supervised criterion is used to select important features, and PFCM is applied to cluster the samples into two clusters. Weights of the samples are calculated based on possibilities and typicality values from the PFCM, and the ground truth labels. A weighted nonlinear SVM is trained. During the test process, when an unknown image is presented, suspicious regions are located with the segmentation step, selected features are extracted, and the suspicious MC regions are classified as containing MC or not by the trained weighted nonlinear SVM. Finally, the MC regions are analyzed with spatial information to locate MC clusters. The proposed method is evaluated using a database of 410 clinical mammograms and compared with a standard unweighted support vector machine (SVM) classifier. The detection performance is evaluated using response receiver operating (ROC) curves and free-response receiver operating characteristic (FROC) curves. The proposed method obtained an area under the ROC curve of 0.8676, while the standard SVM obtained an area of 0.8268 for MC detection. For MC cluster detection, the proposed method obtained a high sensitivity of 92 % with a false-positive rate of 2.3 clusters/ image, and it is also better than standard SVM with 4.7 false-positive clusters/image at the same sensitivity.

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“…However, given the difficulty of differentiation between benign and malignant patterns, many studies usually combine texture features and geometry to perform this task, such as Sahiner et al (2001), Shi et al (2007), Suganthi and Madheswaran (2010), Varela et al (2006), Mu et al (2008) and Liu et al (2010Liu et al ( , 2011.…”

Section: Introductionmentioning

confidence: 99%

Texture analysis of masses in digitized mammograms using Gleason and Menhinick Diversity Indexes

Rocha

Bráz

Silva

et al. 2014

RBEB

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Introduction: Breast cancer is the second most common type of cancer in the world, being more common among women and representing 22% of all new cancer cases every year. The sooner it is diagnosed, the better the chances of a successful treatment are. Mammography is one way to detect non-palpable tumors that cause breast cancer. However, it is known that the sensitivity of this exam can vary considerably due to factors such as the specialist's experience, the patient's age and the quality of the images obtained in the exam. The use of computational techniques involving artificial intelligence and image processing has contributed more and more to support the specialists in obtaining a more precise diagnosis. Methods: This paper proposes a methodology that exclusively uses texture analysis to describe features of masses in digitized mammograms.To increase the efficiency of texture feature extraction, the diversity index's capability to detect patterns of species co-occurrence is used. For this purpose, the Gleason and Menhinick indexes are used. Finally, the extracted texture is classified using the Support Vector Machine, looking to differentiate the malignant masses from the benign. Results: The best result was obtained using the Gleason index, with 86.66% accuracy, 90% sensitivity, 83.33% specificity and an area under the ROC Curve (Az) of 0.86. Conclusion: Both indexes showed statistically similar performance; however, the Gleason index was slightly superior.

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A Benign and Malignant Mass Classification Algorithm Based on an Improved Level Set Segmentation and Texture Feature Analysis

Cited by 23 publications

References 13 publications

Analysis of 2D singularities for mammographic mass classification

Analysis of 2D singularities for mammographic mass classification

Microcalcification detection in full-field digital mammograms with PFCM clustering and weighted SVM-based method

Texture analysis of masses in digitized mammograms using Gleason and Menhinick Diversity Indexes

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