Detection and classification of masses in breast ultrasound images

Shi, Xiangjun; Cheng, Heng-Da; Hu, Liming; Ju, Wen; Tian, Jiawei

doi:10.1016/j.dsp.2009.10.010

Cited by 98 publications

(47 citation statements)

References 18 publications

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“…Joo et al [8] also employed an ANN with morphologic features. Shi et al [9] developed a CAD scheme based on a fuzzy support vector machine to detect and classify mass from ultrasonographic images. Horsch et al [10] extracted lesion shape, margin definition, echogenic texture, and posterior acoustic enhancement or shadowing of masses from ultrasonographic images, and they estimated the likelihood of malignancy by using linear discriminant analysis with these features.…”

Section: Introductionmentioning

confidence: 99%

Computerized Determination Scheme for Histological Classification of Breast Mass Using Objective Features Corresponding to Clinicians’ Subjective Impressions on Ultrasonographic Images

et al. 2013

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It is often difficult for clinicians to decide correctly on either biopsy or follow-up for breast lesions with masses on ultrasonographic images. The purpose of this study was to develop a computerized determination scheme for histological classification of breast mass by using objective features corresponding to clinicians' subjective impressions for image features on ultrasonographic images. Our database consisted of 363 breast ultrasonographic images obtained from 363 patients. It included 150 malignant (103 invasive and 47 noninvasive carcinomas) and 213 benign masses (87 cysts and 126 fibroadenomas). We divided our database into 65 images (28 malignant and 37 benign masses) for training set and 298 images (122 malignant and 176 benign masses) for test set. An observer study was first conducted to obtain clinicians' subjective impression for nine image features on mass. In the proposed method, location and area of the mass were determined by an experienced clinician. We defined some feature extraction methods for each of nine image features. For each image feature, we selected the feature extraction method with the highest correlation coefficient between the objective features and the average clinicians' subjective impressions. We employed multiple discriminant analysis with the nine objective features for determining histological classification of mass. The classification accuracies of the proposed method were 88.4 % (76/86) for invasive carcinomas, 80.6 % (29/36) for noninvasive carcinomas, 86.0 % (92/107) for fibroadenomas, and 84.1 % (58/69) for cysts, respectively. The proposed method would be useful in the differential diagnosis of breast masses on ultrasonographic images as diagnosis aid.

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Section: Introductionmentioning

confidence: 99%

Computerized Determination Scheme for Histological Classification of Breast Mass Using Objective Features Corresponding to Clinicians’ Subjective Impressions on Ultrasonographic Images

et al. 2013

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“…The skewness is zero if the histogram is symmetrical about the mean, and is otherwise either positive or negative depending whether it has been skewed above or below the mean. Thus μ3 is an indication of symmetry [27]. The kurtosis is a measure of flatness of the histogram.…”

Section: Histogram Based-featuresmentioning

confidence: 99%

“…Neural Networks), which minimize the empirical training error. SVM has achieved superior performance in a wide range of applications [4,11,21,23,27]. SVM is designed for binary classification, but in our study, the classification problem is multiple.…”

Section: Introductionmentioning

confidence: 99%

Automated Focal Liver Lesion Staging Classification based on Haralick Texture Features and Multi-SVM

Sakr¹,

Fares²,

Ramadan³

2014

IJCA

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This paper proposes automated identification and classification of various stages of focal liver lesions based on the Multi-Support Vector Machine (Multi-SVM). The proposed system can be used to discriminate focal liver diseases such as Cyst, Hemangioma, and Hepatocellular carcinoma along with normal liver. The multi-class scenario is a composition of a series of two-class problems, using oneagainst-all which is the earliest and one of the most widely used implementations. We formulate the discrimination between cysts, cavernous hemangioma, hepatocellular carcinoma, and normal tissue as a supervised learning problem, and apply Multi-SVM to classify the diseases using Haralick local texture descriptors and histogram based features calculated from Regions Of Interest (ROIs), as input. Selection of ROI significantly impact the classification performances, thus we proposes an automatic ROI selection using Fuzzy c-means initialized by level set technique. For multi-class classification, we adopt the One-Against-All (OAA) method. The proposed Multi-SVM based CAD system using 10-fold cross validation yielded classification accuracy of 96.11% with the individual class accuracy of 97.78%, 95.56%, 93.33% and 97.78% for NOR, Cyst, HEM and HCC cases respectively. The proposed Multi-SVM based system is compared with the K-Nearest Neighbor (KNN) based approaches. Experimental results have demonstrated that the Multi-SVM based system greatly outperforms KNN-based approaches and other methods in the literature. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of focal liver lesion diseases. General TermsMedical Image Processing, Image Features and Analysis, and Liver diagnosis.

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“…Some recent works are performed in automated 3D images. In fact, the 3D ultrasound images can improve the performance over it by exploiting the correlation between the whole tumor in three dimensions (Shi et al, 2010;Sahinern et al, 2009). In order to overcome existing drawbacks in this paper proposed a optimization algorithm for image extraction.…”

Section: Problem Statementmentioning

confidence: 99%

An Weighted Particle Swarm Optimization (Wpso) Technique for Accurate Breast Cancer Detection

2017

IJAERD

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Detection and classification of masses in breast ultrasound images

Cited by 98 publications

References 18 publications

Computerized Determination Scheme for Histological Classification of Breast Mass Using Objective Features Corresponding to Clinicians’ Subjective Impressions on Ultrasonographic Images

Computerized Determination Scheme for Histological Classification of Breast Mass Using Objective Features Corresponding to Clinicians’ Subjective Impressions on Ultrasonographic Images

Automated Focal Liver Lesion Staging Classification based on Haralick Texture Features and Multi-SVM

An Weighted Particle Swarm Optimization (Wpso) Technique for Accurate Breast Cancer Detection

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